Gaussian Process Explained | Papers With Code

Gaussian Process

Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty bounds are baked in with the model.

Image Source: Gaussian Processes for Machine Learning, C. E. Rasmussen & C. K. I. Williams

Latest Papers

PAPER	DATE
CobBO: Coordinate Backoff Bayesian Optimization Jian Tan • Niv Nayman • Mengchang Wang • Rong Jin	2021-01-13
Automated feature selection for data-driven models of rapid battery capacity fade and end of life Samuel Greenbank • David A. Howey	2021-01-12
Regret Analysis of Distributed Gaussian Process Estimation and Coverage Lai Wei • Andrew McDonald • Vaibhav Srivastava	2021-01-12
An asymptotic formula for the variance of the number of zeroes of a stationary Gaussian process Eran Assaf • Jeremiah Buckley • Naomi Feldheim	2021-01-11
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances David Ginsbourger • Cedric Schärer	2021-01-08
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods Ana B. Ruescas • Martin Hieronymi • Sampsa Koponen • Kari Kallio • Gustau Camps-Valls	2021-01-07
Modeling massive multivariate spatial data with the basis graphical lasso Mitchell Krock • William Kleiber • Dorit Hammerling • Stephen Becker	2021-01-07
Infinitely Wide Tensor Networks as Gaussian Process Erdong Guo • David Draper	2021-01-07
Multi-instrumental view of magnetic fields and activity of $ε$ Eridani with SPIRou, NARVAL, and TESS P. Petit • C. P. Folsom • J. -F. Donati • L. Yu • J. -D. do Nascimento Jr. • S. Jeffers • S. C. Marsden • J. Morin • A. A. Vidotto	2021-01-07
Gaussian Function On Response Surface Estimation Mohammadhossein Toutiaee • John Miller	2021-01-04
Gauss-Legendre Features for Gaussian Process Regression Paz Fink Shustin • Haim Avron	2021-01-04
Phase Transitions in Recovery of Structured Signals from Corrupted Measurements Zhongxing Sun • Wei Cui • Yulong Liu	2021-01-03
Meta-Learning Conjugate Priors for Few-Shot Bayesian Optimization Ruduan Plug	2021-01-03
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features Anonymous	2021-01-01
Few-Shot Bayesian Optimization with Deep Kernel Surrogates Anonymous	2021-01-01
Guiding Neural Network Initialization via Marginal Likelihood Maximization Anonymous	2021-01-01
Learning Collision-free Latent Space for Bayesian Optimization Anonymous	2021-01-01
Physics Informed Deep Kernel Learning Anonymous	2021-01-01
Learning What Not to Model: Gaussian Process Regression with Negative Constraints Anonymous	2021-01-01
Large-width functional asymptotics for deep Gaussian neural networks Anonymous	2021-01-01
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels Anonymous	2021-01-01
Anomaly detection and regime searching in fitness-tracker data Anonymous	2021-01-01
Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit Anonymous	2021-01-01
Are wider nets better given the same number of parameters? Anonymous	2021-01-01
A Temporal Kernel Approach for Deep Learning with Continuous-time Information Anonymous	2021-01-01
Optimal Designs of Gaussian Processes with Budgets for Hyperparameter Optimization Anonymous	2021-01-01
Sparse Gaussian Process Variational Autoencoders Anonymous	2021-01-01
Variational Deterministic Uncertainty Quantification Anonymous	2021-01-01
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries Kailong Liu • Xiaosong Hu • Zhongbao Wei • Yi Li • Yan Jiang	2020-12-31
Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials Suresh Kondati Natarajan • Miguel A. Caro	2020-12-31
Social media data reveals signal for public consumer perceptions Neeti Pokhriyal • Abenezer Dara • Benjamin Valentino • Soroush Vosoughi	2020-12-26
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods Suman Majumder • Yawen Guan • Brian J. Reich • Arvind K. Saibaba	2020-12-24
Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions Sumit Jha • Carlos Busso	2020-12-23
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations Jialei Chen • Zhehui Chen • Chuck Zhang • C. F. Jeff Wu	2020-12-22
Gaussian Process Regression constrained by Boundary Value Problems Mamikon Gulian • Ari Frankel • Laura Swiler	2020-12-22
Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation Claus Thorn Ekstrøm • Andreas Kryger Jensen	2020-12-22
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior Anh Tong • Toan Tran • Hung Bui • Jaesik Choi	2020-12-21
A Bayesian methodology for localising acoustic emission sources in complex structures Matthew R. Jones • Tim J. Rogers • Keith Worden • Elizabeth J. Cross	2020-12-21
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach Yvonne R. Stürz • Mohammad Khosravi • Roy S. Smith	2020-12-20
Guiding Neural Network Initialization via Marginal Likelihood Maximization Anthony S. Tai • Chunfeng Huang	2020-12-17
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach MohammadNoor Injadat • Abdallah Moubayed • Abdallah Shami	2020-12-16
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes Anna Mateo-Sanchis • Jordi Munoz-Mari • Manuel Campos-Taberner • Javier Garcia-Haro • Gustau Camps-Valls	2020-12-11
Exact Bayesian inference for level-set Cox processes Flavio B. Gonçalves • Barbara C. C. Dias	2020-12-10
On the Environmental Variability of Offshore Wind Power Behzad Golparvar • Petros Papadopoulos • Ahmed Aziz Ezzat • Ruo-Qian Wang	2020-12-08
Retrieval of Case 2 Water Quality Parameters with Machine Learning Ana B. Ruescas • Gonzalo Mateo-Garcia • Gustau Camps-Valls • Martin Hieronymi	2020-12-08
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method Katja Berger • Jochem Verrelst • Jean-Baptiste Féret • Tobias Hank • Matthias Wocher • Wolfram Mauser • Gustau Camps-Valls	2020-12-07
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables Taehyeon Kim • Jaeyeon Ahn • Nakyil Kim • Seyoung Yun	2020-12-07
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients YiFan Li • Chao Li • Stephen Price • Carola-Bibiane Schönlieb • Xi Chen	2020-12-05
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes Luca Pipia • Jordi Muñoz-Marí • Eatidal Amin • Santiago Belda • Gustau Camps-Valls • Jochem Verrelst	2020-12-05
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems Giulio Ortali • Nicola Demo • Gianluigi Rozza	2020-12-03
A similarity-based Bayesian mixture-of-experts model Tianfang Zhang • Rasmus Bokrantz • Jimmy Olsson	2020-12-03
Gaussian Process-based Approach for Bilevel Optimization in Power Systems -- A Critical Load Restoration Case Yang Liu • Hung D. Nguyen	2020-12-02
A Gaussian Process-based Price-Amount Curve Construction for Demand Response Provided by Internet Data Centers Yang Liu • Hung D. Nguyen	2020-12-02
The temporal overfitting problem with applications in wind power curve modeling Abhinav Prakash • Rui Tuo • Yu Ding	2020-12-02
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication Rupert Mitchell • Jan Blumenkamp • Amanda Prorok	2020-12-01
Identifying signal and noise structure in neural population activity with Gaussian process factor models Stephen Keeley • Mikio Aoi • Yiyi Yu • Spencer Smith • Jonathan W. Pillow	2020-12-01
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data Virginia Rutten • Alberto Bernacchia • Maneesh Sahani • Guillaume Hennequin	2020-12-01
Neuronal Gaussian Process Regression \| Johannes Friedrich	2020-12-01
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes Hao Chen • Lili Zheng • Raed Al Kontar • Garvesh Raskutti	2020-12-01
Generalised Bayesian Filtering via Sequential Monte Carlo Ayman Boustati • Omer Deniz Akyildiz • Theodoros Damoulas • Adam Johansen	2020-12-01
An emulator for the Lyman-$α$ forest in beyond-$Λ$CDM cosmologies Christian Pedersen • Andreu Font-Ribera • Keir K. Rogers • Patrick McDonald • Hiranya V. Peiris • Andrew Pontzen • Anže Slosar	2020-11-30
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes Morgane Menz • Sylvain Dubreuil • Jérôme Morio • Christian Gogu • Nathalie Bartoli • Marie Chiron	2020-11-30
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals Zejian Liu • Meng Li	2020-11-27
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors Clemens Hutter • Moritz von Stosch • Mariano Nicolas Cruz Bournazou • Alessandro Butté	2020-11-27
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope Jungtaek Kim • Minsu Cho • Seungjin Choi	2020-11-26
Computational Model of Motion Sickness Describing the Effects of Learning Exogenous Motion Dynamics Takahiro Wada	2020-11-25
Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics \| Rama K. Vasudevan • Kyle Kelley • Hiroshi Funakubo • Stephen Jesse • Sergei V. Kalinin • Maxim Ziatdinov	2020-11-25
Equivariant Conditional Neural Processes Peter Holderrieth • Michael Hutchinson • Yee Whye Teh	2020-11-25
Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR): a Python module for representing multidimensional functions with machine-learned lower-dimensional terms Owen Ren • Mohamed Ali Boussaidi • Dmitry Voytsekhovsky • Sergei Manzhos	2020-11-24
Data-aided Sensing for Gaussian Process Regression in IoT Systems Jinho Choi	2020-11-23
Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization Gauthier Guinet • Valerio Peronne • Cédric Archambeau	2020-11-23
Robust Gaussian Process Regression Based on Iterative Trimming Zhao-Zhou Li • Lu Li • Zhengyi Shao	2020-11-22
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly Cheolhei Lee • Jianguo Wu • Wenjia Wang • Xiaowei Yue	2020-11-21
Central and Non-central Limit Theorems arising from the Scattering Transform and its Neural Activation Generalization Gi-Ren Liu • Yuan-Chung Sheu • Hau-Tieng Wu	2020-11-21
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry Yizhou Qian • Mojtaba Forghani • Jonghyun Harry Lee • Matthew Farthing • Tyler Hesser • Peter Kitanidis • Eric Darve	2020-11-19
Learning Interpretable Flight's 4D Landing Parameters Using Tunnel Gaussian Process Sim Kuan Goh • Narendra Pratap Singh • Zhi Jun Lim • Sameer Alam	2020-11-18
Understanding Variational Inference in Function-Space David R. Burt • Sebastian W. Ober • Adrià Garriga-Alonso • Mark van der Wilk	2020-11-18
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects Fernando Castañeda • Jason J. Choi • Bike Zhang • Claire J. Tomlin • Koushil Sreenath	2020-11-14
Towards Human-Level Learning of Complex Physical Puzzles Kei Ota • Devesh K. Jha • Diego Romeres • Jeroen van Baar • Kevin A. Smith • Takayuki Semitsu • Tomoaki Oiki • Alan Sullivan • Daniel Nikovski • Joshua B. Tenenbaum	2020-11-14
Factorized Gaussian Process Variational Autoencoders \| Metod Jazbec • Michael Pearce • Vincent Fortuin	2020-11-14
Towards NNGP-guided Neural Architecture Search Daniel S. Park • Jaehoon Lee • Daiyi Peng • Yuan Cao • Jascha Sohl-Dickstein	2020-11-11
Energy consumption forecasting using a stacked nonparametric Bayesian approach Dilusha Weeraddana • Nguyen Lu Dang Khoa • Lachlan O Neil • Weihong Wang • Chen Cai	2020-11-11
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds Erik Drysdale • Devin Singh • Anna Goldenberg	2020-11-09
Pathwise Conditioning of Gaussian Processes James T. Wilson • Viacheslav Borovitskiy • Alexander Terenin • Peter Mostowsky • Marc Peter Deisenroth	2020-11-08
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang • Shengyang Sun • Roger Grosse	2020-11-06
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders Rohit Batra • Hanjun Dai • Tran Doan Huan • Lihua Chen • Chiho Kim • Will R. Gutekunst • Le Song • Rampi Ramprasad	2020-11-04
Bayesian Variational Optimization for Combinatorial Spaces Tony C. Wu • Daniel Flam-Shepherd • Alán Aspuru-Guzik	2020-11-03
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process A. Daneshkhah • M. Mousavi Nezhad • O. Chatrabgoun • M. Esmaeilbeigi • T. Sedighi • S. Abolfathi	2020-11-03
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix Julian Krebs • Hervé Delingette • Nicholas Ayache • Tommaso Mansi	2020-11-03
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment Xingyu Lei • Student Member • Zhifang Yang • Member • Junbo Zhao • Juan Yu • Senior Member • IEEE	2020-11-02
Sample-efficient reinforcement learning using deep Gaussian processes Charles Gadd • Markus Heinonen • Harri Lähdesmäki • Samuel Kaski	2020-11-02
Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks Zoe L. de Beurs • Andrew Vanderburg • Christopher J. Shallue • Xavier Dumusque • Andrew Collier Cameron • Lars A. Buchhave • Rosario Cosentino • Adriano Ghedina • Raphaëlle D. Haywood • Nicholas Langellier • David W. Latham • Mercedes López-Morales • Michel Mayor • Giusi Micela • Timothy W. Milbourne • Annelies Mortier • Emilio Molinari • Francesco Pepe • David F. Phillips • Matteo Pinamonti • Giampaolo Piotto • Ken Rice • Dimitar Sasselov • Alessandro Sozzetti • Stéphane Udry • Christopher A. Watson	2020-10-30
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective \| Vu Nguyen • Vaden Masrani • Rob Brekelmans • Michael A. Osborne • Frank Wood	2020-10-29
Matern Gaussian Processes on Graphs Viacheslav Borovitskiy • Iskander Azangulov • Alexander Terenin • Peter Mostowsky • Marc Peter Deisenroth • Nicolas Durrande	2020-10-29
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models Sudeep Salgia • Sattar Vakili • Qing Zhao	2020-10-27
Are wider nets better given the same number of parameters? Anna Golubeva • Behnam Neyshabur • Guy Gur-Ari	2020-10-27
Scalable Gaussian Process Variational Autoencoders \| Metod Jazbec • Vincent Fortuin • Michael Pearce • Stephan Mandt • Gunnar Rätsch	2020-10-26
Variational Bayesian Unlearning Quoc Phong Nguyen • Bryan Kian Hsiang Low • Patrick Jaillet	2020-10-24
Ranking Creative Language Characteristics in Small Data Scenarios Julia Siekiera • Marius Köppel • Edwin Simpson • Kevin Stowe • Iryna Gurevych • Stefan Kramer	2020-10-23
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference Sean Plummer • Shuang Zhou • Anirban Bhattacharya • David Dunson • Debdeep Pati	2020-10-23
Sparse Gaussian Process Variational Autoencoders Matthew Ashman • Jonathan So • William Tebbutt • Vincent Fortuin • Michael Pearce • Richard E. Turner	2020-10-20
Federated Bayesian Optimization via Thompson Sampling Zhongxiang Dai • Kian Hsiang Low • Patrick Jaillet	2020-10-20
Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen • Christabella Irwanto • Arno Solin	2020-10-19
Movement-induced Priors for Deep Stereo Yuxin Hou • Muhammad Kamran Janjua • Juho Kannala • Arno Solin	2020-10-18
KrigHedge: GP Surrogates for Delta Hedging Mike Ludkovski • Yuri Saporito	2020-10-16
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces Francesco Romor • Marco Tezzele • Gianluigi Rozza	2020-10-16
Diffusion Based Gaussian Processes on Restricted Domains David B Dunson • Hau-Tieng Wu • Nan Wu	2020-10-14
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit Ben Adlam • Jaehoon Lee • Lechao Xiao • Jeffrey Pennington • Jasper Snoek	2020-10-14
Local Differential Privacy for Bayesian Optimization Xingyu Zhou • Jian Tan	2020-10-13
Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion Sebastian Haan • Fabio Ramos • Dietmar Müller	2020-10-12
Online Learning and Distributed Control for Residential Demand Response Xin Chen • YingYing Li • Jun Shimada • Na Li	2020-10-11
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids Tong Ma • David Alonso Barajas-Solano • Ramakrishna Tipireddy • Alexandre M. Tartakovsky	2020-10-09
Dynamic mode decomposition for forecasting and analysis of power grid load data Daniel Dylewsky • David Barajas-Solano • Tong Ma • Alexandre M. Tartakovsky • J. Nathan Kutz	2020-10-08
Emulator-based global sensitivity analysis for flow-like landslide run-out models Hu Zhao • Florian Amann • Julia Kowalski	2020-10-08
Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach \| Mona Fuhrländer • Sebastian Schöps	2020-10-08
Bayesian Optimized Monte Carlo Planning John Mern • Anil Yildiz • Zachary Sunberg • Tapan Mukerji • Mykel J. Kochenderfer	2020-10-07
Splitting Gaussian Process Regression for Streaming Data Nick Terry • Youngjun Choe	2020-10-06
Gaussian Process Models with Low-Rank Correlation Matrices for Both Continuous and Categorical Inputs Dominik Kirchhoff • Sonja Kuhnt	2020-10-06
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features Agustinus Kristiadi • Matthias Hein • Philipp Hennig	2020-10-06
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty Casey Stowers • Taeksang Lee • Ilias Bilionis • Arun Gosain • Adrian Buganza Tepole	2020-10-05
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models Paul Westermann • Ralph Evins	2020-10-05
Short-term prediction of photovoltaic power generation using Gaussian process regression Yahya Al Lawati • Jack Kelly • Dan Stowell	2020-10-05
BOSS: Bayesian Optimization over String Spaces Henry B. Moss • Daniel Beck • Javier Gonzalez • David S. Leslie • Paul Rayson	2020-10-02
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets Banus Jaume • Sermesant Maxime • Camara Oscar • Lorenzi Marco	2020-10-02
Gravitational wave peak luminosity model for precessing binary black holes Afura Taylor • Vijay Varma	2020-09-30
Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering Joris Guerin • Anne Magaly de Paula Canuto • Luiz Marcos Garcia Goncalves	2020-09-29
Multi-task Causal Learning with Gaussian Processes Virginia Aglietti • Theodoros Damoulas • Mauricio Álvarez • Javier González	2020-09-27
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems Bruno Henrique Groenner Barbosa • Nan Xu • Hassan Askari • Amir Khajepour	2020-09-25
Stein Variational Gaussian Processes Thomas Pinder • Christopher Nemeth • David Leslie	2020-09-25
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization Ehecatl Antonio del Rio-Chanona • Panagiotis Petsagkourakis • Eric Bradford • Jose Eduardo Alves Graciano • Benoit Chachuat	2020-09-18
Cross-Entropy Method Variants for Optimization \| Robert J. Moss	2020-09-18
Mean-Variance Analysis in Bayesian Optimization under Uncertainty Shogo Iwazaki • Yu Inatsu • Ichiro Takeuchi	2020-09-17
Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments Xi Chen • Yu Guan • Jian-Qing Shi • Xiu-Li Du • Janet Eyre	2020-09-17
Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes Shihao Yang • Samuel W. K. Wong • S. C. Kou	2020-09-16
On Information Gain and Regret Bounds in Gaussian Process Bandits Sattar Vakili • Kia Khezeli • Victor Picheny	2020-09-15
Interpolating the Trace of the Inverse of Matrix $\mathbf{A} + t \mathbf{B}$ Siavash Ameli • Shawn C. Shadden	2020-09-15
Tracking disease outbreaks from sparse data with Bayesian inference Bryan Wilder • Michael J. Mina • Milind Tambe	2020-09-12
Symplectic Gaussian Process Regression of Hamiltonian Flow Maps Katharina Rath • Christopher G. Albert • Bernd Bischl • Udo von Toussaint	2020-09-11
Generalized Multi-Output Gaussian Process Censored Regression Daniele Gammelli • Kasper Pryds Rolsted • Dario Pacino • Filipe Rodrigues	2020-09-10
A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands R. G. Seymour • T. Kypraios • P. D. O'Neill • T. J. Hagenaars	2020-09-09
$\mathcal{RL}_1$-$\mathcal{GP}$: Safe Simultaneous Learning and Control Aditya Gahlawat • Arun Lakshmanan • Lin Song • Andrew Patterson • Zhuohuan Wu • Naira Hovakimyan • Evangelos Theodorou	2020-09-08
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People Maxime De Bois • Mounîm A. El Yacoubi • Mehdi Ammi	2020-09-08
Physics-informed Gaussian Process for Online Optimization of Particle Accelerators Adi Hanuka • X. Huang • J. Shtalenkova • D. Kennedy • A. Edelen • V. R. Lalchand • D. Ratner • J. Duris	2020-09-08
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition Alistair Shilton • Sunil Gupta • Santu Rana • Svetha Venkatesh	2020-09-08
Augmented Gaussian Random Field: Theory and Computation Sheng Zhang • Xiu Yang • Samy Tindel • Guang Lin	2020-09-03
Real Image Super Resolution Via Heterogeneous Model using GP-NAS Zhihong Pan • Baopu Li • Teng Xi • Yanwen Fan • Gang Zhang • Jingtuo Liu • Junyu Han • Errui Ding	2020-09-02
Non-parametric generalized linear model Matthew Dowling • Yuan Zhao • Il Memming Park	2020-09-02
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments Cedric Le Gentil • Mallikarjuna Vayugundla • Riccardo Giubilato • Wolfgang Stürzl • Teresa Vidal-Calleja • Rudolph Triebel	2020-09-01
Modulating Scalable Gaussian Processes for Expressive Statistical Learning \| Haitao Liu • Yew-Soon Ong • Xiaomo Jiang • Xiaofang Wang	2020-08-29
Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips Daiki Otaki • Hirofumi Nonaka • Noboru Yamada	2020-08-28
Fast Bayesian Force Fields from Active Learning: Study of Inter-Dimensional Transformation of Stanene \| Yu Xie • Jonathan Vandermause • Lixin Sun • Andrea Cepellotti • Boris Kozinsky	2020-08-26
Variable selection for Gaussian process regression through a sparse projection Chiwoo Park • David J. Borth • Nicholas S. Wilson • Chad N. Hunter	2020-08-25
Exoplanet Validation with Machine Learning: 50 new validated Kepler planets David J. Armstrong • Jevgenij Gamper • Theodoros Damoulas	2020-08-24
Fast Approximate Multi-output Gaussian Processes \| Vladimir Joukov • Dana Kulić	2020-08-22
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization Xu Cai • Jonathan Scarlett	2020-08-20
Improving predictions of Bayesian neural networks via local linearization Alexander Immer • Maciej Korzepa • Matthias Bauer	2020-08-19
Bayesian neural networks and dimensionality reduction Deborshee Sen • Theodore Papamarkou • David Dunson	2020-08-18
Preferential Bayesian optimisation with Skew Gaussian Processes Alessio Benavoli • Dario Azzimonti • Dario Piga	2020-08-15
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems Waad Subber • Sayan Ghosh • Piyush Pandita • Yiming Zhang • Liping Wang	2020-08-14
Continuous Optimization Benchmarks by Simulation \| Martin Zaefferer • Frederik Rehbach	2020-08-14
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations Elena Arcari • Andrea Carron • Melanie N. Zeilinger	2020-08-13
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP Sungho Yoon • Ayoung Kim	2020-08-12
Deep State-Space Gaussian Processes Zheng Zhao • Muhammad Emzir • Simo Särkkä	2020-08-11
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes Kentaro Mitsui • Tomoki Koriyama • Hiroshi Saruwatari	2020-08-07
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression Yixiang Deng • Guang Lin • Xiu Yang	2020-08-03
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships Nina Kudryashova • Theoklitos Amvrosiadis • Nathalie Dupuy • Nathalie Rochefort • Arno Onken	2020-08-03
OFAI-UKP at [email protected]: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning Tristan Miller • Erik-Lân Do Dinh • Edwin Simpson • Iryna Gurevych	2020-08-03
Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee • Samuel S. Schoenholz • Jeffrey Pennington • Ben Adlam • Lechao Xiao • Roman Novak • Jascha Sohl-Dickstein	2020-07-31
Cold Posteriors and Aleatoric Uncertainty Ben Adlam • Jasper Snoek • Samuel L. Smith	2020-07-31
Random Forests for dependent data Arkajyoti Saha • Sumanta Basu • Abhirup Datta	2020-07-30
Regression modelling with I-priors Wicher Bergsma • Haziq Jamil	2020-07-30
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification \| Santiago Toledo-Cortés • Melissa De La Pava • Oscar Perdómo • Fabio A. González	2020-07-29
Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment \| A. F. López-Lopera • D. Idier • J. Rohmer • F. Bachoc	2020-07-28
Bayesian Dynamic Mapping of an Exo-Earth from Photometric Variability \| Hajime Kawahara • Kento Masuda	2020-07-26
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation Romit Maulik • Themistoklis Botsas • Nesar Ramachandra • Lachlan Robert Mason • Indranil Pan	2020-07-23
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes Arthur Leroy • Pierre Latouche • Benjamin Guedj • Servane Gey	2020-07-21
Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks Alexander Immer	2020-07-21
Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil Matheus Henrique Dal Molin Ribeiro • Ramon Gomes da Silva • Viviana Cocco Mariani • Leandro dos Santos Coelho	2020-07-21
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization Yang Yang • Ke Deng • Michael Zhu	2020-07-20
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes Jake Snell • Richard Zemel	2020-07-20
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach Yun Yuan • Qinzheng Wang • Xianfeng Terry Yang	2020-07-17
Ordinal Regression with Fenton-Wilkinson Order Statistics: A Case Study of an Orienteering Race Joonas Pääkkönen	2020-07-14
Highway Traffic State Estimation Using Physics Regularized Gaussian Process: Discretized Formulation Yun Yuan • Zhao Zhang • Xianfeng Terry Yang	2020-07-14
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes \| William J. Wilkinson • Paul E. Chang • Michael Riis Andersen • Arno Solin	2020-07-12
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis Zirui Li • Chao Lu • Cheng Gong • Cheng Gong • Jinghang Li • Lianzhen Wei	2020-07-11
Numerical simulation, clustering and prediction of multi-component polymer precipitation Pavan Inguva • Lachlan Mason • Indranil Pan • Miselle Hengardi • Omar K. Matar	2020-07-10
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers Tom Lovett • Mark Briers • Marcos Charalambides • Radka Jersakova • James Lomax • Chris Holmes	2020-07-09
Understanding the effect of hyperparameter optimization on machine learning models for structure design problems Xianping Du • Hongyi Xu • Feng Zhu	2020-07-04
Gaussian Process Regression with Local Explanation Yuya Yoshikawa • Tomoharu Iwata	2020-07-03
BOSH: Bayesian Optimization by Sampling Hierarchically Henry B. Moss • David S. Leslie • Paul Rayson	2020-07-02
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis Elena Raponi • Hao Wang • Mariusz Bujny • Simonetta Boria • Carola Doerr	2020-07-02
Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers Ridwan Alam • David B. Peden • John C. Lach	2020-07-02
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes Ali Hebbal • Loic Brevault • Mathieu Balesdent • El-Ghazali Talbi • Nouredine Melab	2020-06-29
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang • Daniel R. Jiang • Maximilian Balandat • Brian Karrer • Jacob R. Gardner • Roman Garnett	2020-06-29
Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process Liwei Wang • Siyu Tao • Ping Zhu • Wei Chen	2020-06-27
Prediction with Gaussian Process Dynamical Models Thomas Beckers • Sandra Hirche	2020-06-25
Epoch-evolving Gaussian Process Guided Learning Jiabao Cui • Xuewei Li • Bin Li • Hanbin Zhao • Bourahla Omar • Xi Li	2020-06-25
Heat kernel and intrinsic Gaussian processes on manifolds Ke Ye • Mu Niu • Pokman Cheung	2020-06-25
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization Antonio Candelieri • Riccardo Perego • Francesco Archetti	2020-06-25
Likelihood-Free Gaussian Process for Regression \| Yuta Shikuri	2020-06-24
Pareto Active Learning with Gaussian Processes and Adaptive Discretization Andi Nika • Kerem Bozgan • Çağın Ararat • Cem Tekin	2020-06-24
Variational Orthogonal Features David R. Burt • Carl Edward Rasmussen • Mark van der Wilk	2020-06-23
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors \| Yuling Yao • Aki Vehtari • Andrew Gelman	2020-06-22
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure Shogo Iwazaki • Yu Inatsu • Ichiro Takeuchi	2020-06-22
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes \| Mengdi Xu • Wenhao Ding • Jiacheng Zhu • Zuxin Liu • Baiming Chen • Ding Zhao	2020-06-19
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev • Henri Pesonen • Markus Heinonen • Jukka Corander • Samuel Kaski	2020-06-18
Exact posterior distributions of wide Bayesian neural networks Jiri Hron • Yasaman Bahri • Roman Novak • Jeffrey Pennington • Jascha Sohl-Dickstein	2020-06-18
GPIRT: A Gaussian Process Model for Item Response Theory JBrandon Duck-Mayr • Roman Garnett • Jacob M. Montgomery	2020-06-17
Longitudinal Variational Autoencoder Siddharth Ramchandran • Gleb Tikhonov • Miika Koskinen • Harri Lähdesmäki	2020-06-17
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Zhe Liu • Zi Lin • Shreyas Padhy • Dustin Tran • Tania Bedrax-Weiss • Balaji Lakshminarayanan	2020-06-17
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges Laura Swiler • Mamikon Gulian • Ari Frankel • Cosmin Safta • John Jakeman	2020-06-16
Real-Time Regression with Dividing Local Gaussian Processes Armin Lederer • Alejandro Jose Ordonez Conejo • Korbinian Maier • Wenxin Xiao • Sandra Hirche	2020-06-16
Sparse Gaussian Process Based On Hat Basis Functions Wenqi Fang • Huiyun Li • Hui Huang • Shaobo Dang • Zhejun Huang • Zheng Wang	2020-06-15
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi • Felix Berkenkamp • Andreas Krause	2020-06-15
Variational Bayesian Monte Carlo with Noisy Likelihoods \| Luigi Acerbi	2020-06-15
GP3: A Sampling-based Analysis Framework for Gaussian Processes Armin Lederer • Markus Kessler • Sandra Hirche	2020-06-14
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression Wenxin Xiao • Armin Lederer • Sandra Hirche	2020-06-14
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search Vu Nguyen • Tam Le • Makoto Yamada • Michael A Osborne	2020-06-13
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel Binxin Ru • Xingchen Wan • Xiaowen Dong • Michael Osborne	2020-06-13
Automated Measurement of Quasar Redshift with a Gaussian Process Leah Fauber • Ming-Feng Ho • Simeon Bird • Christian R. Shelton • Roman Garnett • Ishita Korde	2020-06-12
Gaussian Processes on Graphs via Spectral Kernel Learning Yin-Cong Zhi • Yin Cheng Ng • Xiaowen Dong	2020-06-12
Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamás Erdélyi • Cameron Musco • Christopher Musco	2020-06-12
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher T. Jensen • Ta-Chu Kao • Marco Tripodi • Guillaume Hennequin	2020-06-12
SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers • Daniel Palenicek • Vincent Moens • Mohammed Abdullah • Aivar Sootla • Jun Wang • Haitham Ammar	2020-06-12
Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis MohammadReza Ebrahimi • Navona Calarco • Kieran Campbell • Colin Hawco • Aristotle Voineskos • Ashish Khisti	2020-06-10
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model Tianjian Zhou • Yuan Ji	2020-06-10
Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor • Theofanis Karaletsos • Thang D. Bui	2020-06-09
Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu • Xing Liu • Ruya Kang • Zhichao Shen • Seth Flaxman • François-Xavier Briol	2020-06-09
Scalable Thompson Sampling using Sparse Gaussian Process Models Sattar Vakili • Victor Picheny • Artem Artemev	2020-06-09
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder Alex Campbell • Pietro Liò	2020-06-08
Schrödinger PCA: You Only Need Variances for Eigenmodes Ziming Liu • Sitian Qian • Yixuan Wang • Yuxuan Yan • Tianyi Yang	2020-06-08
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation \| Julian Berk • Sunil Gupta • Santu Rana • Svetha Venkatesh	2020-06-08
Physics Regularized Gaussian Processes Zheng Wang • Wei Xing • Robert Kirby • Shandian Zhe	2020-06-08
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation Zheng Wang • Wei Xing • Robert Kirby • Shandian Zhe	2020-06-08
Learning compositional models of robot skills for task and motion planning Zi Wang • Caelan Reed Garrett • Leslie Pack Kaelbling • Tomás Lozano-Pérez	2020-06-08
A conditional one-output likelihood formulation for multitask Gaussian processes Vanessa Gómez-Verdejo • Óscar García-Hinde • Manel Martínez-Ramón	2020-06-05
Health Indicator Forecasting for Improving Remaining Useful Life Estimation Qiyao Wang • Ahmed Farahat • Chetan Gupta • Haiyan Wang	2020-06-05
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels Jarred Barber	2020-06-05
Quadruply Stochastic Gaussian Processes Trefor W. Evans • Prasanth B. Nair	2020-06-04
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules Michele Fraccaroli • Evelina Lamma • Fabrizio Riguzzi	2020-06-03
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels Marcus M. Noack • Gregory S. Doerk • Ruipeng Li • Jason K. Streit • Richard A. Vaia • Kevin G. Yager • Masafumi Fukuto	2020-06-03
Quantifying the Uncertainty in Model Parameters Using Gaussian Process-Based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models Jwala Dhamala • John L. Sapp • B. Milan Horácek • Linwei Wang	2020-06-02
Semi-supervised deep learning for high-dimensional uncertainty quantification Zequn Wang • Mingyang Li	2020-06-01
GP-NAS: Gaussian Process Based Neural Architecture Search Zhihang Li • Teng Xi • Jiankang Deng • Gang Zhang • Shengzhao Wen • Ran He	2020-06-01
Video Instance Segmentation Tracking With a Modified VAE Architecture Chung-Ching Lin • Ying Hung • Rogerio Feris • Linglin He	2020-06-01
Bayesian Restoration of Audio Degraded by Low-Frequency Pulses Modeled via Gaussian Process Hugo Tremonte de Carvalho • Flávio Rainho Ávila • Luiz Wagner Pereira Biscainho	2020-05-28
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks Jinglin Zhang • Wenjun Xu • Hui Gao • Miao Pan • Zhu Han • Ping Zhang	2020-05-28
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure \| Koorosh Aslansefat • Ioannis Sorokos • Declan Whiting • Ramin Tavakoli Kolagari • Yiannis Papadopoulos # 1 CLASSIFICATION ON XOR	2020-05-27
How Training Data Impacts Performance in Learning-based Control Armin Lederer • Alexandre Capone • Jonas Umlauft • Sandra Hirche	2020-05-25
Path Imputation Strategies for Signature Models of Irregular Time Series Michael Moor • Max Horn • Christian Bock • Karsten Borgwardt • Bastian Rieck	2020-05-25
Longitudinal Deep Kernel Gaussian Process Regression Junjie Liang • Yanting Wu • Dongkuan Xu • Vasant Honavar	2020-05-24
MANGO: A Python Library for Parallel Hyperparameter Tuning \| Sandeep Singh Sandha • Mohit Aggarwal • Igor Fedorov • Mani Srivastava	2020-05-22
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation Yifan Chen • Houman Owhadi • Andrew M. Stuart	2020-05-22
Data-driven Efficient Solvers and Predictions of Conformational Transitions for Langevin Dynamics on Manifold in High Dimensions Yuan Gao • Jian-Guo Liu • Nan Wu	2020-05-22
Global Optimization of Gaussian processes Artur M. Schweidtmann • Dominik Bongartz • Daniel Grothe • Tim Kerkenhoff • Xiaopeng Lin • Jaromil Najman • Alexander Mitsos	2020-05-21
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide Kyle P Messier • Matthias Katzfuss	2020-05-19
Deep Latent-Variable Kernel Learning \| Haitao Liu • Yew-Soon Ong • Xiaomo Jiang • Xiaofang Wang	2020-05-18
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes Lai Wei • Xiaobo Tan • Vaibhav Srivastava	2020-05-18
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober • Laurence Aitchison	2020-05-17
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes Zhaozhi Qian • Ahmed M. Alaa • Mihaela van der Schaar	2020-05-13
Parameter Inference for Weak Lensing using Gaussian Processes and MOPED Arrykrishna Mootoovaloo • Alan F. Heavens • Andrew H. Jaffe • Florent Leclercq	2020-05-13
Machine learning based digital twin for dynamical systems with multiple time-scales Souvik Chakraborty • Sondipon Adhikari	2020-05-12
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS Shubhanshu Shekhar • Tara Javidi	2020-05-11
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design Rémy Priem • Nathalie Bartoli • Youssef Diouane • Alessandro Sgueglia	2020-05-11
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires Moonseop Kim • Huayi Yin • Guang Lin	2020-05-11
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques Moajjem Hossain Chowdhury • Md Nazmul Islam Shuzan • Muhammad E. H. Chowdhury • Zaid B Mahbub • M. Monir Uddin • Amith Khandakar • Mamun Bin Ibne Reaz	2020-05-07
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition Steven I Reeves • Dongwook Lee • Anurag Singh • Kunal Verma	2020-05-07
Planning from Images with Deep Latent Gaussian Process Dynamics Nathanael Bosch • Jan Achterhold • Laura Leal-Taixé • Jörg Stückler	2020-05-07
Active Preference-Based Gaussian Process Regression for Reward Learning \| Erdem Bıyık • Nicolas Huynh • Mykel J. Kochenderfer • Dorsa Sadigh	2020-05-06
Using Machine Learning to Emulate Agent-Based Simulations Claudio Angione • Eric Silverman • Elisabeth Yaneske	2020-05-05
Localized active learning of Gaussian process state space models Alexandre Capone • Jonas Umlauft • Thomas Beckers • Armin Lederer • Sandra Hirche	2020-05-04
Evaluation of Deep Gaussian Processes for Text Classification P. Jayashree • P. K. Srijith	2020-05-01
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition Raul Quintero • Ignacio Parra • David Fernandez Llorca • Miguel Angel Sotelo	2020-04-30
Continual Deep Learning by Functional Regularisation of Memorable Past Pingbo Pan • Siddharth Swaroop • Alexander Immer • Runa Eschenhagen • Richard E. Turner • Mohammad Emtiyaz Khan	2020-04-29
Guided search for desired functional responses via Bayesian optimization of generative model: hysteresis loop shape engineering in ferroelectrics Sergei V. Kalinin • Maxim Ziatdinov • Rama K. Vasudevan	2020-04-27
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage Xiaowei Yue • Yuchen Wen • Jeffrey H. Hunt • Jianjun Shi	2020-04-23
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck Alec Koppel • Hrusikesha Pradhan • Ketan Rajawat	2020-04-23
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes \| A. Rene Geist • Sebastian Trimpe	2020-04-23
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity Sam Coveney • Cesare Corrado • Caroline H Roney • Daniel O'Hare • Steven E Williams • Mark D O'Neill • Steven A Niederer • Richard H Clayton • Jeremy E Oakley • Richard D Wilkinson	2020-04-22
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit Tomoki Koriyama • Hiroshi Saruwatari	2020-04-22
Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework Sandrine Gümbel • Thorsten Schmidt	2020-04-16
Gaussian Process Learning-based Probabilistic Optimal Power Flow Parikshit Pareek • Hung D. Nguyen	2020-04-16
Foreground modelling via Gaussian process regression: an application to HERA data Abhik Ghosh • Florent Mertens • Gianni Bernardi • Mário G. Santos • Nicholas S. Kern • Christopher L. Carilli • Trienko L. Grobler • Léon V. E. Koopmans • Daniel C. Jacobs • Adrian Liu • Aaron R. Parsons • Miguel F. Morales • James E. Aguirre • Joshua S. Dillon • Bryna J. Hazelton • Oleg M. Smirnov • Bharat K. Gehlot • Siyanda Matika • Paul Alexander • Zaki S. Ali • Adam P. Beardsley • Roshan K. Benefo • Tashalee S. Billings • Judd D. Bowman • Richard F. Bradley • Carina Cheng • Paul M. Chichura • David R. DeBoer • Eloy de Lera Acedo • Aaron Ewall-Wice • Gcobisa Fadana • Nicolas Fagnoni • Austin F. Fortino • Randall Fritz • Steve R. Furlanetto • Samavarti Gallardo • Brian Glendenning • Deepthi Gorthi • Bradley Greig • Jasper Grobbelaar • Jack Hickish • Alec Josaitis • Austin Julius • Amy S. Igarashi • MacCalvin Kariseb • Saul A. Kohn • Matthew Kolopanis • Telalo Lekalake • Anita Loots • David MacMahon • Lourence Malan • Cresshim Malgas • Matthys Maree • Zachary E. Martinot • Nathan Mathison • Eunice Matsetela • Andrei Mesinger • Abraham R. Neben • Bojan Nikolic • Chuneeta D. Nunhokee • Nipanjana Patra • Samantha Pieterse • Nima Razavi-Ghods • Jon Ringuette • James Robnett • Kathryn Rosie • Raddwine Sell • Craig Smith • Angelo Syce • Max Tegmark • Nithyanandan Thyagarajan • Peter K. G. Williams • Haoxuan Zheng	2020-04-13
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties \| Richard Cheng • Mohammad Javad Khojasteh • Aaron D. Ames • Joel W. Burdick	2020-04-11
Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation Sergei V. Kalinin • Mani Valleti • Rama K. Vasudevan • Maxim Ziatdinov	2020-04-09
Online Constrained Model-based Reinforcement Learning Benjamin van Niekerk • Andreas Damianou • Benjamin Rosman	2020-04-07
Direct loss minimization algorithms for Bayesian predictors Yadi Wei • Rishit Sheth • Roni Khardon	2020-04-07
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes Moyan Li • Raed Kontar	2020-04-06
Gaussian Process Boosting \| Fabio Sigrist	2020-04-06
Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction Constantinos Daskalakis • Petros Dellaportas • Aristeidis Panos	2020-04-03
Predicting the outputs of finite networks trained with noisy gradients Gadi Naveh • Oded Ben-David • Haim Sompolinsky • Zohar Ringel	2020-04-02
Projection Pursuit Gaussian Process Regression Gecheng Chen • Rui Tuo	2020-04-01
A Blackbox Yield Estimation Workflow with Gaussian Process Regression Applied to the Design of Electromagnetic Devices \| Mona Fuhrländer • Sebastian Schöps	2020-03-30
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data Leif Erik Andersson • Bart Doekemeijer • Daan van der Hoek • Jan-Willem van Wingerden • Lars Imsland	2020-03-30
Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory Ali Reza Pedram • Takashi Tanaka	2020-03-27
On Infinite-Width Hypernetworks Etai Littwin • Tomer Galanti • Lior Wolf • Greg Yang	2020-03-27
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles Max Veit • David M. Wilkins • Yang Yang • Robert A. DiStasio Jr. • Michele Ceriotti	2020-03-27
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains \| Michele Peruzzi • Sudipto Banerjee • Andrew O. Finley	2020-03-25
Preferential Batch Bayesian Optimization Eero Siivola • Akash Kumar Dhaka • Michael Riis Andersen • Javier Gonzalez • Pablo Garcia Moreno • Aki Vehtari	2020-03-25
Detecting Multiple DLAs per Spectrum in SDSS DR12 with Gaussian Processes Ming-Feng Ho • Simeon Bird • Roman Garnett	2020-03-24
Efficient Gaussian Process Bandits by Believing only Informative Actions Amrit Singh Bedi • Dheeraj Peddireddy • Vaneet Aggarwal • Alec Koppel	2020-03-23
Analysis of Greenhouse Gases Shalin Shah	2020-03-21
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation Xiong Lyu • Mike Ludkovski	2020-03-19
Gaze-Sensing LEDs for Head Mounted Displays Kaan Akşit • Jan Kautz • David Luebke	2020-03-18
Gaussian process aided function comparison using noisy scattered data Abhinav Prakash • Rui Tuo • Yu Ding	2020-03-17
The Elliptical Processes: a New Family of Flexible Stochastic Processes Maria Bånkestad • Jens Sjölund • Jalil Taghia • Thomas Schön	2020-03-13
Data-driven surrogate modelling and benchmarking for process equipment Gabriel F. N. Gonçalves • Assen Batchvarov • Yuyi Liu • Yuxin Liu • Lachlan Mason • Indranil Pan • Omar K. Matar	2020-03-13
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time Hideaki Imamura • Nontawat Charoenphakdee • Futoshi Futami • Issei Sato • Junya Honda • Masashi Sugiyama	2020-03-10
Channel Attention with Embedding Gaussian Process: A Probabilistic Methodology Jiyang Xie • Dongliang Chang • Zhanyu Ma • Guoqiang Zhang • Jun Guo	2020-03-10
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization Antonio Candelieri • Ilaria Giordani • Riccardo Perego • Francesco Archetti	2020-03-09
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing Feng Yin • Zhidi Lin • Yue Xu • Qinglei Kong • Deshi Li • Sergios Theodoridis • Shuguang • Cui	2020-03-08
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi • Markus Heinonen • Edwin V. Bonilla • Zheyang Shen • Maurizio Filippone	2020-03-06
Multi-Output Gaussian Processes for Multi-Population Longevity Modeling Nhan Huynh • Mike Ludkovski	2020-03-05
Corruption-Tolerant Gaussian Process Bandit Optimization Ilija Bogunovic • Andreas Krause • Jonathan Scarlett	2020-03-04
Regression via Implicit Models and Optimal Transport Cost Minimization \| Saurav Manchanda • Khoa Doan • Pranjul Yadav • S. Sathiya Keerthi	2020-03-03
Gaussian Process Policy Optimization Ashish Rao • Bidipta Sarkar • Tejas Narayanan	2020-03-02
Imbalance Learning for Variable Star Classification \| Zafiirah Hosenie • Robert Lyon • Benjamin Stappers • Arrykrishna Mootoovaloo • Vanessa McBride	2020-02-27
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients Sébastien Petit • Julien Bect • Sébastien da Veiga • Paul Feliot • Emmanuel Vazquez	2020-02-26
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements Alberto Dalla Libera • Diego Romeres • Devesh K. Jha • Bill Yerazunis • Daniel Nikovski	2020-02-25
Generalized Bayesian Filtering via Sequential Monte Carlo Ayman Boustati • Ömer Deniz Akyildiz • Theodoros Damoulas • Adam Johansen	2020-02-23
Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast Fatemeh Najibi • Dimitra Apostolopoulou • Eduardo Alonso	2020-02-23
Nonmyopic Gaussian Process Optimization with Macro-Actions Dmitrii Kharkovskii • Chun Kai Ling • Kian Hsiang Low	2020-02-22
Efficiently Sampling Functions from Gaussian Process Posteriors \| James T. Wilson • Viacheslav Borovitskiy • Alexander Terenin • Peter Mostowsky • Marc Peter Deisenroth	2020-02-21
Development of modeling and control strategies for an approximated Gaussian process Shisheng Cui • Chia-Jung Chang	2020-02-12
Regret Bounds for Noise-Free Bayesian Optimization Sattar Vakili • Victor Picheny • Nicolas Durrande	2020-02-12
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data Johannes A. Stork • Todor Stoyanov	2020-02-12
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation Subhayan De • Jolene Britton • Matthew Reynolds • Ryan Skinner • Kenneth Jansen • Alireza Doostan	2020-02-11
Graph Convolutional Gaussian Processes For Link Prediction Felix L. Opolka • Pietro Liò	2020-02-11
Gaussian process imputation of multiple financial series Taco de Wolff • Alejandro Cuevas • Felipe Tobar	2020-02-11
Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems Xiaoran Ruan • Ke Li • Bilel Derbel • Arnaud Liefooghe	2020-02-08
Deep Moment Matching Kernel for Multi-source Gaussian Processes Chi-Ken Lu • Patrick Shafto	2020-02-07
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications Yun Yuan • Xianfeng Terry Yang • Zhao Zhang • Shandian Zhe	2020-02-06
One-Shot Bayes Opt with Probabilistic Population Based Training Jack Parker-Holder • Vu Nguyen • Stephen Roberts	2020-02-06
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction Andrew Patterson • Aditya Gahlawat • Naira Hovakimyan	2020-02-05
Data-driven high-fidelity prediction of the equivalent sand-grain height of rough surfaces Mostafa Aghaei Jouybari • Junlin Yuan • Giles J. Brereton	2020-02-04
Uncertainty Quantification for Bayesian Optimization Rui Tuo • Wenjia Wang	2020-02-04
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation Madhusanka Manimel Wadu • Sumudu Samarakoon • Mehdi Bennis	2020-02-03
Linearly Constrained Gaussian Processes with Boundary Conditions Markus Lange-Hegermann	2020-02-03
A memory of motion for visual predictive control tasks Antonio Paolillo • Teguh Santoso Lembono • Sylvain Calinon	2020-01-31
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions Toni Karvonen • George Wynne • Filip Tronarp • Chris J. Oates • Simo Särkkä	2020-01-29
Convergence Guarantees for Gaussian Process Means with Misspecified Likelihoods and Smoothness George Wynne • François-Xavier Briol • Mark Girolami	2020-01-29
TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model Yusuke Uchiyama • Kei Nakagawa	2020-01-29
Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption Peter Fenner • Edward O. Pyzer-Knapp	2020-01-28
Bayesian nonparametric shared multi-sequence time series segmentation Olga Mikheeva • Ieva Kazlauskaite • Hedvig Kjellström • Carl Henrik Ek	2020-01-27
Bayesian optimization for backpropagation in Monte-Carlo tree search Yueqin Li • Nengli Lim	2020-01-25
The role of surrogate models in the development of digital twins of dynamic systems Souvik Chakraborty • Sondipon Adhikari • Ranjan Ganguli	2020-01-25
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes \| Daniele Gammelli • Inon Peled • Filipe Rodrigues • Dario Pacino • Haci A. Kurtaran • Francisco C. Pereira	2020-01-21
Projection based Active Gaussian Process Regression for Pareto Front Modeling Zhengqi Gao • Jun Tao • Yangfeng Su • Dian Zhou • Xuan Zeng	2020-01-20
Scalable Hyperparameter Optimization with Lazy Gaussian Processes \| Raju Ram • Sabine Müller • Franz-Josef Pfreundt • Nicolas R. Gauger • Janis Keuper	2020-01-16
Doubly Sparse Variational Gaussian Processes Vincent Adam • Stefanos Eleftheriadis • Nicolas Durrande • Artem Artemev • James Hensman	2020-01-15
Robust Gaussian Process Regression with a Bias Model Chiwoo Park • David J. Borth • Nicholas S. Wilson • Chad N. Hunter • Fritz J. Friedersdorf	2020-01-14
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process Siming Bayer • Ute Spiske • Jie Luo • Tobias Geimer • William M. Wells III • Martin Ostermeier • Rebecca Fahrig • Arya Nabavi • Christoph Bert • Ilker Eyupoglo • Andreas Maier	2020-01-12
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering Yohan Jung • Jinkyoo Park	2020-01-07
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes Devanshu Agrawal • Theodore Papamarkou • Jacob Hinkle	2020-01-03
Learning Human Postural Control with Hierarchical Acquisition Functions Nils Rottmann • Tjasa Kunavar • Jan Babic • Jan Peters • Elmar Rueckert	2020-01-01
Learning Neural Surrogate Model for Warm-Starting Bayesian Optimization Haotian Zhang • Jian Sun • Zongben Xu	2020-01-01
Approximate Inference for Fully Bayesian Gaussian Process Regression Vidhi Lalchand • Carl Edward Rasmussen	2019-12-31
Disentangling Trainability and Generalization in Deep Neural Networks Lechao Xiao • Jeffrey Pennington • Samuel S. Schoenholz	2019-12-30
A statistical test for correspondence of texts to the Zipf-Mandelbrot law Anik Chakrabarty • Mikhail Chebunin • Artyom Kovalevskii • Ilya Pupyshev • Natalia Zakrevskaya • Qianqian Zhou	2019-12-25
Simulation of Turbulent Flow around a Generic High-Speed Train using Hybrid Models of RANS Numerical Method with Machine Learning Alireza Hajipour • Arash Mirabdolah Lavasani • Mohammad Eftekhari Yazdi • Amir Mosavi • Shahaboddin Shamshirband • Kwok-Wing Chau	2019-12-25
On Simulation and Trajectory Prediction with Gaussian Process Dynamics Lukas Hewing • Elena Arcari • Lukas P. Fröhlich • Melanie N. Zeilinger	2019-12-23
Tensor Basis Gaussian Process Models of Hyperelastic Materials Ari Frankel • Reese Jones • Laura Swiler	2019-12-23
Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang • Christian J. Walder • Edwin V. Bonilla • Marian-Andrei Rizoiu • Lexing Xie	2019-12-21
Methods for comparing uncertainty quantifications for material property predictions Kevin Tran • Willie Neiswanger • Junwoong Yoon • Eric Xing • Zachary W. Ulissi	2019-12-20
SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes Stephen M. Feeney • Benjamin D. Wandelt • Melissa K. Ness	2019-12-19
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems \| Wei Huang • Richard Yi Da Xu	2019-12-19
Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures \| Taehee Lee • Charles E. Lawrence	2019-12-18
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design Piyush Pandita • Nimish Awalgaonkar • Ilias Bilionis • Jitesh Panchal	2019-12-16
Active emulation of computer codes with Gaussian processes -- Application to remote sensing Daniel Heestermans Svendsen • Luca Martino • Gustau Camps-Valls	2019-12-13
On the relationship between multitask neural networks and multitask Gaussian Processes Karthikeyan K • Shubham Kumar Bharti • Piyush Rai	2019-12-12
Learning and Optimization with Bayesian Hybrid Models Elvis A. Eugene • Xian Gao • Alexander W. Dowling	2019-12-12
Tensor Completion via Gaussian Process Based Initialization Yermek Kapushev • Ivan Oseledets • Evgeny Burnaev	2019-12-11
Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax Daniel T. Chang	2019-12-11
Frequentist Consistency of Generalized Variational Inference Jeremias Knoblauch	2019-12-10
Location Trace Privacy Under Conditional Priors Casey Meehan • Kamalika Chaudhuri	2019-12-09
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies \| Juho Timonen • Henrik Mannerström • Aki Vehtari • Harri Lähdesmäki	2019-12-07
Ordinal Bayesian Optimisation Victor Picheny • Sattar Vakili • Artem Artemev	2019-12-05
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression Tong Teng • Jie Chen • Yehong Zhang • Kian Hsiang Low	2019-12-05
Numerical Gaussian process Kalman filtering Armin Küper • Steffen Waldherr	2019-12-03
Nonnegative Gaussian process tomography for generalized segmented planar detectors D. Blyth • N. Mullins • E. Galyaev • J. Holmes	2019-12-02
Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes Rui Li	2019-12-01
Infra-slow brain dynamics as a marker for cognitive function and decline Shagun Ajmera Shyam Sunder Ajmera • Shreya Rajagopal • Razi Rehman • Devarajan Sridharan	2019-12-01
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes \| Lingge Li • Dustin Pluta • Babak Shahbaba • Norbert Fortin • Hernando Ombao • Pierre Baldi	2019-12-01
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes \| Greg Yang	2019-12-01
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network Shuhan Zhang • Wenlong Lyu • Fan Yang • Changhao Yan • Dian Zhou • Xuan Zeng	2019-12-01
Richer priors for infinitely wide multi-layer perceptrons Russell Tsuchida • Fred Roosta • Marcus Gallagher	2019-11-29
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach Mohamed K. Abdel-Aziz • Sumudu Samarakoon • Mehdi Bennis • Walid Saad	2019-11-27
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling Maxim Ziatdinov • Dohyung Kim • Sabine Neumayer • Rama K. Vasudevan • Liam Collins • Stephen Jesse • Mahshid Ahmadi • Sergei V. Kalinin	2019-11-26
Actively Learning Gaussian Process Dynamics Mona Buisson-Fenet • Friedrich Solowjow • Sebastian Trimpe	2019-11-22
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model \| Juan-José Giraldo • Mauricio A. Álvarez	2019-11-22
Towards a complete 3D morphable model of the human head \| Stylianos Ploumpis • Evangelos Ververas • Eimear O' Sullivan • Stylianos Moschoglou • Haoyang Wang • Nick Pears • William A. P. Smith • Baris Gecer • Stefanos Zafeiriou	2019-11-18
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference Leen Alawieh • Jonathan Goodman • John B. Bell	2019-11-17
Causal inference using Bayesian non-parametric quasi-experimental design \| Max Hinne • Marcel A. J. van Gerven • Luca Ambrogioni	2019-11-15
Conjugate Gradients for Kernel Machines Simon Bartels • Philipp Hennig	2019-11-14
Uncertainty on Asynchronous Time Event Prediction \| Marin Biloš • Bertrand Charpentier • Stephan Günnemann	2019-11-13
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness Zhu Li • Adrian Perez-Suay • Gustau Camps-Valls • Dino Sejdinovic	2019-11-11
Bayesian Active Learning for Structured Output Design Kota Matsui • Shunya Kusakawa • Keisuke Ando • Kentaro Kutsukake • Toru Ujihara • Ichiro Takeuchi	2019-11-09
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees Michael Maiworm • Daniel Limon • Rolf Findeisen	2019-11-08
Non-parametric Probabilistic Load Flow using Gaussian Process Learning Parikshit Pareek • Chuan Wang • Hung D. Nguyen	2019-11-08
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models Pavel Berkovich • Eric Perim • Wessel Bruinsma	2019-11-05
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data Akitoshi Masuda • Yoshihiko Susuki • Manel Martínez-Ramón • Andrea Mammoli • Atsushi Ishigame	2019-11-04
On Batch Bayesian Optimization Sayak Ray Chowdhury • Aditya Gopalan	2019-11-04
Seasonally-Adjusted Auto-Regression of Vector Time Series Enzo Busseti	2019-11-04
Online tuning and light source control using a physics-informed Gaussian process Adi A. Hanuka • J. Duris • J. Shtalenkova • D. Kennedy • A. Edelen • D. Ratner • X. Huang	2019-11-04
Enhanced Convolutional Neural Tangent Kernels Zhiyuan Li • Ruosong Wang • Dingli Yu • Simon S. Du • Wei Hu • Ruslan Salakhutdinov • Sanjeev Arora	2019-11-03
Continual Multi-task Gaussian Processes \| Pablo Moreno-Muñoz • Antonio Artés-Rodríguez • Mauricio A. Álvarez	2019-10-31
Safe Exploration for Interactive Machine Learning Matteo Turchetta • Felix Berkenkamp • Andreas Krause	2019-10-30
Function-Space Distributions over Kernels \| Gregory W. Benton • Wesley J. Maddox • Jayson P. Salkey • Julio Albinati • Andrew Gordon Wilson	2019-10-29
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes \| Greg Yang	2019-10-28
Convolutional Sequence Generation for Skeleton-Based Action Synthesis \| Sijie Yan • Zhizhong Li • Yuanjun Xiong • Huahan Yan	2019-10-27
Implicit Posterior Variational Inference for Deep Gaussian Processes \| Haibin Yu • Yizhou Chen • Zhongxiang Dai • Kian Hsiang Low • Patrick Jaillet	2019-10-26
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search \| Colin White • Willie Neiswanger • Yash Savani # 4 NEURAL ARCHITECTURE SEARCH ON NAS-BENCH-201, IMAGENET-16-120	2019-10-25
Sparse Orthogonal Variational Inference for Gaussian Processes \| Jiaxin Shi • Michalis K. Titsias • Andriy Mnih	2019-10-23
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform Alexander Schaefer • Lukas Luft • Wolfram Burgard	2019-10-23
Generalised learning of time-series: Ornstein-Uhlenbeck processes Mehmet Süzen • Alper Yegenoglu	2019-10-21
Bayesian Optimization Allowing for Common Random Numbers Michael Pearce • Matthias Poloczek • Juergen Branke	2019-10-21
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison	2019-10-17
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation \| Ryan-Rhys Griffiths • Miguel Garcia-Ortegon • Alexander A. Aldrick • Alpha A. Lee	2019-10-17
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics Chirag Modi • Uros Seljak	2019-10-16
Parametric Gaussian Process Regressors Martin Jankowiak • Geoff Pleiss • Jacob R. Gardner	2019-10-16
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue • Raed Kontar	2019-10-15
Regularized Sparse Gaussian Processes Rui Meng • Herbert Lee • Soper Braden • Priyadip Ray	2019-10-13
Bayesian Optimization using Pseudo-Points Chao Qian • Hang Xiong • Ke Xue	2019-10-12
Evolving Gaussian Process kernels from elementary mathematical expressions Ibai Roman • Roberto Santana • Alexander Mendiburu • Jose A. Lozano	2019-10-11
Learning from demonstration with model-based Gaussian process Noémie Jaquier • David Ginsbourger • Sylvain Calinon	2019-10-11
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks Julius von Kügelgen • Paul K Rubenstein • Bernhard Schölkopf • Adrian Weller	2019-10-09
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression Tong Ma • Renke Huang • David Barajas-Solano • Ramakrishna Tipireddy • Alexandre M. Tartakovsky	2019-10-09
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression Weiyang Zhang • Wenshuo Wang • Ding Zhao	2019-10-08
mfEGRA: Multifidelity Efficient Global Reliability Analysis Anirban Chaudhuri • Alexandre N. Marques • Karen E. Willcox	2019-10-06
Operational Calibration: Debugging Confidence Errors for DNNs in the Field Zenan Li • Xiaoxing Ma • Chang Xu • Jingwei Xu • Chun Cao • Jian Lü	2019-10-06
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning Sahand Rezaei-Shoshtari • David Meger • Inna Sharf	2019-10-05
Dynamic Embedding on Textual Networks via a Gaussian Process \| Pengyu Cheng • Yitong Li • Xinyuan Zhang • Liqun Cheng • David Carlson • Lawrence Carin	2019-10-05
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables Yichi Zhang • Daniel Apley • Wei Chen	2019-10-03
Kepler data analysis: non-Gaussian noise and Fourier Gaussian process analysis of star variability Jakob Robnik • Uroš Seljak	2019-10-02
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis Margherita Rosnati • Vincent Fortuin	2019-09-27
Deep recurrent Gaussian process with variational Sparse Spectrum approximation Roman Föll • Bernard Haasdonk • Markus Hanselmann • Holger Ulmer	2019-09-27
Debiased Bayesian inference for average treatment effects \| Kolyan Ray • Botond Szabo	2019-09-26
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario Thanh Le • Vasant Honavar	2019-09-25
Adversarial Vulnerability Bounds for Gaussian Process Classification Michael Thomas Smith • Kathrin Grosse • Michael Backes • Mauricio A Alvarez	2019-09-19
Bayesian Optimization under Heavy-tailed Payoffs \| Sayak Ray Chowdhury • Aditya Gopalan	2019-09-16
MCTS-based Automated Negotiation Agent Cédric Buron • Zahia Guessoum • Sylvain Ductor	2019-09-12
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning Arpan Kusari • Jonathan P. How	2019-09-11
Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model Yohei Sawada	2019-09-09
Estimating Fingertip Forces, Torques, and Local Curvatures from Fingernail Images Nutan Chen • Göran Westling • Benoni B. Edin • Patrick van der Smagt	2019-09-09
Unsupervised Image Regression for Heterogeneous Change Detection Luigi T. Luppino • Filippo M. Bianchi • Gabriele Moser • Stian N. Anfinsen	2019-09-07
Deep kernel learning for integral measurements Carl Jidling • Johannes Hendriks • Thomas B. Schön • Adrian Wills	2019-09-04
Latent Gaussian process with composite likelihoods for data-driven disease stratification Siddharth Ramchandran • Miika Koskinen • Harri Lähdesmäki	2019-09-04
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning Lixue Cheng • Nikola B. Kovachki • Matthew Welborn • Thomas F. Miller III	2019-09-04
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis Ziqi Wang • Marco Broccardo	2019-08-27
Finite size corrections for neural network Gaussian processes Joseph M. Antognini	2019-08-27
Using Contextual Information to Improve Blood Glucose Prediction Mohammad Akbari • Rumi Chunara	2019-08-24
Adaptive Configuration Oracle for Online Portfolio Selection Methods Favour M. Nyikosa • Michael A. Osborne • Stephen J. Roberts	2019-08-22
Are Registration Uncertainty and Error Monotonically Associated Jie Luo • Sarah Frisken • Duo Wang • Alexandra Golby • Masashi Sugiyama • William M. Wells III	2019-08-21
Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy Collin A. Politsch • Jessi Cisewski-Kehe • Rupert A. C. Croft • Larry Wasserman	2019-08-20
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models Guoli Song • Shuhui Wang • Qingming Huang • Qi Tian	2019-08-14
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension Ye Xue • Diego Klabjan • Yuan Luo	2019-08-12
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold YoungJoon Yoo • Sangdoo Yun • Hyung Jin Chang • Yiannis Demiris • Jin Young Choi	2019-08-12
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations \| Ksenia Korovina • Sailun Xu • Kirthevasan Kandasamy • Willie Neiswanger • Barnabas Poczos • Jeff Schneider • Eric P. Xing	2019-08-05
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology Chundra Cathcart	2019-08-01
Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression Sevda Shabani • Saeed Samadianfard • Mohammad Taghi Sattari • Shahab Shamshirband • Amir Mosavi • Tibor Kmet • Annamaria R. Varkonyi-Koczy	2019-08-01
Scalable Bayesian Non-linear Matrix Completion Xiangju Qin • Paul Blomstedt • Samuel Kaski	2019-07-31
Sequential Learning of Active Subspaces Nathan Wycoff • Mickael Binois • Stefan M. Wild	2019-07-26
Bayesian Volumetric Autoregressive generative models for better semisupervised learning \| Guilherme Pombo • Robert Gray • Tom Varsavsky • John Ashburner • Parashkev Nachev	2019-07-26
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty Sayan Ghosh • Jesper Kristensen • Yiming Zhang • Waad Subber • Liping Wang	2019-07-26
Towards Scalable Gaussian Process Modeling Piyush Pandita • Jesper Kristensen • Liping Wang	2019-07-25
Accelerating Experimental Design by Incorporating Experimenter Hunches Cheng Li • Santu Rana • Sunil Gupta • Vu Nguyen • Svetha Venkatesh • Alessandra Sutti • David Rubin • Teo Slezak • Murray Height • Mazher Mohammed • Ian Gibson	2019-07-22
A Multiple Continuous Signal Alignment Algorithm with Gaussian Process Profiles and an Application to Paleoceanography \| Taehee Lee • Lorraine E. Lisiecki • Devin Rand • Geoffrey Gebbie • Charles E. Lawrence	2019-07-20
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs Yusuke Tanaka • Toshiyuki Tanaka • Tomoharu Iwata • Takeshi Kurashima • Maya Okawa • Yasunori Akagi • Hiroyuki Toda	2019-07-19
Kernel Mode Decomposition and programmable/interpretable regression networks \| Houman Owhadi • Clint Scovel • Gene Ryan Yoo	2019-07-19
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach Abdallah A. Chehade • Ala A. Hussein	2019-07-19
Structured Variational Inference in Unstable Gaussian Process State Space Models \| Silvan Melchior • Sebastian Curi • Felix Berkenkamp • Andreas Krause	2019-07-16
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach Bin Liu • Yu Qi • Ke-Jia Chen	2019-07-15
The Use of Gaussian Processes in System Identification Simo Särkkä	2019-07-13
Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification Felix Batsch • Alireza Daneshkhah • Madeline Cheah • Stratis Kanarachos • Anthony Baxendale	2019-07-11
Gaussian Processes for Analyzing Positioned Trajectories in Sports Yuxin Zhao • Feng Yin • Fredrik Gunnarsson • Fredrik Hultkrantz	2019-07-05
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design Akshay Iyer • Yichi Zhang • Aditya Prasad • Siyu Tao • Yixing Wang • Linda Schadler • L Catherine Brinson • Wei Chen	2019-07-04
Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels Daniel Augusto R. M. A. de Souza • César Lincoln C. Mattos • João Paulo P. Gomes	2019-07-03
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches Yuqing Zhang • Neil Walton	2019-07-02
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time \| Christoph Heindl • Thomas Pönitz • Gernot Stübl • Andreas Pichler • Josef Scharinger	2019-07-01
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning Edwin Simpson • Erik-L{\^a}n Do Dinh • Tristan Miller • Iryna Gurevych	2019-07-01
Multi-objective multi-generation Gaussian process optimizer for design optimization Xiaobiao Huang • Minghao Song • Zhe Zhang	2019-06-29
Learning Fair Representations for Kernel Models \| Zilong Tan • Samuel Yeom • Matt Fredrikson • Ameet Talwalkar	2019-06-27
Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics \| Sebastian Riedel • Freek Stulp	2019-06-27
Modulating Surrogates for Bayesian Optimization Erik Bodin • Markus Kaiser • Ieva Kazlauskaite • Zhenwen Dai • Neill D. F. Campbell • Carl Henrik Ek	2019-06-26
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field \| Yaohui Guo • Vinay Varma Kalidindi • Mansur Arief • Wenshuo Wang • Jiacheng Zhu • Huei Peng • Ding Zhao	2019-06-25
Compositionally-Warped Gaussian Processes Gonzalo Rios • Felipe Tobar	2019-06-23
Sparse Spectrum Gaussian Process for Bayesian Optimization Ang Yang • Cheng Li • Santu Rana • Sunil Gupta • Svetha Venkatesh	2019-06-21
Tomographic Reconstruction of Triaxial Strain Fields from Bragg-Edge Neutron Imaging \| J. N. Hendriks • A. W. T. Gregg • R. R. Jackson • C. M. Wensrich • A. Wills • A. S. Tremsin • T. Shinohara • V. Luzin • O. Kirstein	2019-06-20
Bayesian Optimisation over Multiple Continuous and Categorical Inputs \| Binxin Ru • Ahsan S. Alvi • Vu Nguyen • Michael A. Osborne • Stephen J Roberts	2019-06-20
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances \| Csaba Toth • Harald Oberhauser	2019-06-19
Bayesian inverse regression for dimension reduction with small datasets Xin Cai • Guang Lin • Jinglai Li	2019-06-19
Multi-resolution Multi-task Gaussian Processes \| Oliver Hamelijnck • Theodoros Damoulas • Kangrui Wang • Mark Girolami	2019-06-19
Bayesian Optimization with Binary Auxiliary Information Yehong Zhang • Zhongxiang Dai • Kian Hsiang Low	2019-06-17
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models \| Alessandro Davide Ialongo • Mark van der Wilk • James Hensman • Carl Edward Rasmussen	2019-06-13
Robust Regression for Safe Exploration in Control Anqi Liu • Guanya Shi • Soon-Jo Chung • Anima Anandkumar • Yisong Yue	2019-06-13
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective Omry Cohen • Or Malka • Zohar Ringel	2019-06-12
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics Jacobo Roa-Vicens • Cyrine Chtourou • Angelos Filos • Francisco Rullan • Yarin Gal • Ricardo Silva	2019-06-11
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories Guangyi Zhang • Reza Ashrafi • Anne Juuti • Kirsi Pietiläinen • Pekka Marttinen	2019-06-10
Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study \| Chao Zhai • Hung D. Nguyen	2019-06-09
A Variant of Gaussian Process Dynamical Systems Jing Zhao • Jingjing Fei • Shiliang Sun	2019-06-09
Structured Variational Inference in Continuous Cox Process Models \| Virginia Aglietti • Edwin V. Bonilla • Theodoros Damoulas • Sally Cripps	2019-06-07
A General $\mathcal{O}(n^2)$ Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM Linning Xu • Feng Yin • Jiawei Zhang • Zhi-Quan Luo • Shuguang Cui	2019-06-06
Bayesian Optimization of Composite Functions \| Raul Astudillo • Peter I. Frazier	2019-06-04
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control Armin Lederer • Jonas Umlauft • Sandra Hirche	2019-06-04
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement Ming Lin • Xiaomin Song • Qi Qian • Hao Li • Liang Sun • Shenghuo Zhu • Rong Jin	2019-06-03
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process Sahar Zafari • Mariia Murashkina • Tuomas Eerola • Jouni Sampo • Heikki Kälviäinen • Heikki Haario	2019-06-03
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel \| Xin Qiu • Elliot Meyerson • Risto Miikkulainen	2019-06-03
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes Li-Fang Cheng • Bianca Dumitrascu • Michael Zhang • Corey Chivers • Michael Draugelis • Kai Li • Barbara E. Engelhardt	2019-06-01
Neural Likelihoods for Multi-Output Gaussian Processes Martin Jankowiak • Jacob Gardner	2019-05-31
Enriched Mixtures of Gaussian Process Experts Charles W. L. Gadd • Sara Wade • Alexis Boukouvalas	2019-05-30
Efficient EM-Variational Inference for Hawkes Process Feng Zhou • Zhidong Li • Xuhui Fan • Yang Wang • Arcot Sowmya • Fang Chen	2019-05-29
Adversarial Robustness Guarantees for Classification with Gaussian Processes Arno Blaas • Andrea Patane • Luca Laurenti • Luca Cardelli • Marta Kwiatkowska • Stephen Roberts	2019-05-28
Recursive Estimation for Sparse Gaussian Process Regression \| Manuel Schürch • Dario Azzimonti • Alessio Benavoli • Marco Zaffalon	2019-05-28
Scalable Training of Inference Networks for Gaussian-Process Models \| Jiaxin Shi • Mohammad Emtiyaz Khan • Jun Zhu	2019-05-27
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation \| Théo Galy-Fajou • Florian Wenzel • Christian Donner • Manfred Opper	2019-05-23
Learning spectrograms with convolutional spectral kernels Zheyang Shen • Markus Heinonen • Samuel Kaski	2019-05-23
A Bulirsch-Stoer algorithm using Gaussian processes Philip G. Breen • Christopher N. Foley	2019-05-23
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation Joseph Guinness	2019-05-20
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization Jungtaek Kim • Seungjin Choi	2019-05-18
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations Timur Takhtaganov • Zarija Lukic • Juliane Mueller • Dmitriy Morozov	2019-05-17
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal • Jonathan H. Huggins • Brian Trippe • Tamara Broderick	2019-05-16
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions \| Tim Pearce • Russell Tsuchida • Mohamed Zaki • Alexandra Brintrup • Andy Neely	2019-05-15
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes Chengyao Sun • Weisi Guo	2019-05-15
Online Anomaly Detection with Sparse Gaussian Processes Jingjing Fei • Shiliang Sun	2019-05-14
Adaptive surrogate models for parametric studies Jan N. Fuhg	2019-05-12
Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization Kanrun Huang • Tucker Hermans	2019-05-10
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models \| Francisco Sahli Costabal • Paris Perdikaris • Ellen Kuhl • Daniel E. Hurtado	2019-05-09
Knowing The What But Not The Where in Bayesian Optimization Vu Nguyen • Michael A. Osborne	2019-05-07
A deep learning approach for analyzing the composition of chemometric data Muhammad Bilal • Mohib Ullah	2019-05-07
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations \| Marko Järvenpää • Michael Gutmann • Aki Vehtari • Pekka Marttinen	2019-05-03
Modular Deep Probabilistic Programming \| Zhenwen Dai • Eric Meissner • Neil D. Lawrence	2019-05-01
A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics Alberto Dalla Libera • Ruggero Carli	2019-04-30
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms Kaifeng Yang • Michael Emmerich • André Deutz • Thomas Bäck	2019-04-26
A Bayesian Approach for the Robust Optimisation of Expensive-To-Evaluate Functions Nicholas D. Sanders • Richard M. Everson • Jonathan E. Fieldsend • Alma A. M. Rahat	2019-04-25
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees Jeremiah Zhe Liu	2019-04-21
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes Ognjen Rudovic • Yuria Utsumi • Ricardo Guerrero • Kelly Peterson • Daniel Rueckert • Rosalind W. Picard	2019-04-19
A Bayesian Perspective on the Deep Image Prior \| Zezhou Cheng • Matheus Gadelha • Subhransu Maji • Daniel Sheldon	2019-04-16
Multi-View Stereo by Temporal Nonparametric Fusion \| Yuxin Hou • Juho Kannala • Arno Solin	2019-04-12
Scalarizing Functions in Bayesian Multiobjective Optimization Tinkle Chugh	2019-04-11
Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms Md. Maniruzzaman • Md. Jahanur Rahman • Benojir Ahammed • Md. Menhazul Abedin • Harman S. Suri • Mainak Biswas • Ayman El-Baz • Petros Bangeas • Georgios Tsoulfas • Jasjit S. Suri	2019-04-08
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources Edwin Simpson • Steven Reece • Stephen J. Roberts	2019-04-05
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification Andrew Patterson • Arun Lakshmanan • Naira Hovakimyan	2019-04-04
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events Jonathan Vandermause • Steven B. Torrisi • Simon Batzner • Alexie M. Kolpak • Boris Kozinsky	2019-04-03
Sentiment analysis with genetically evolved Gaussian kernels Ibai Roman • Alexander Mendiburu • Roberto Santana • Jose A. Lozano	2019-04-01
MCTS-based Automated Negotiation Agent (Extended Abstract) Cédric Buron • Zahia Guessoum • Sylvain Ductor	2019-03-29
A Gaussian process latent force model for joint input-state estimation in linear structural systems Rajdip Nayek • Souvik Chakraborty • Sriram Narasimhan	2019-03-29
Using Gaussian process regression for efficient parameter reconstruction Philipp-Immanuel Schneider • Martin Hammerschmidt • Lin Zschiedrich • Sven Burger	2019-03-28
Pixelation is NOT Done in Videos Yet Jizhe Zhou • Chi-Man Pun • YingYu Wang	2019-03-26
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints Nimish Awalgaonkar • Ilias Bilionis • Xiaoqi Liu • Panagiota Karava • Athanasios Tzempelikos	2019-03-21
Deep Gaussian Processes for Multi-fidelity Modeling Kurt Cutajar • Mark Pullin • Andreas Damianou • Neil Lawrence • Javier González	2019-03-18
Financial Applications of Gaussian Processes and Bayesian Optimization Joan Gonzalvez • Edmond Lezmi • Thierry Roncalli • Jiali Xu	2019-03-12
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations Astrid Dahl • Edwin V. Bonilla	2019-03-10
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data Seokhyun Chung • Raed Kontar	2019-03-09
Rates of Convergence for Sparse Variational Gaussian Process Regression \| David R. Burt • Carl E. Rasmussen • Mark van der Wilk	2019-03-08
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation Jiameng Fan • Wenchao Li	2019-03-06
A Bayesian Approach to Triaxial Strain Tomography from High-energy X-ray Diffraction J. N. Hendriks • C. M. Wensrich • A. Wills	2019-03-06
Deep Learning and Gaussian Process based Band Assignment in Dual Band Systems Daoud Burghal • Rui Wang • Andreas F. Molisch	2019-02-28
Estimating Local Function Complexity via Mixture of Gaussian Processes Danny Panknin • Shinichi Nakajima • Thanh Binh Bui • Klaus-Robert Müller	2019-02-27
Active learning via informed search in movement parameter space for efficient robot task learning and transfer \| Nemanja Rakicevic • Petar Kormushev	2019-02-21
Stable Bayesian Optimisation via Direct Stability Quantification Alistair Shilton • Sunil Gupta • Santu Rana • Svetha Venkatesh • Majid Abdolshah • Dang Nguyen	2019-02-21
Bayesian optimisation under uncertain inputs Rafael Oliveira • Lionel Ott • Fabio Ramos	2019-02-21
Gaussian Process Priors for Dynamic Paired Comparison Modelling \| Martin Ingram	2019-02-20
Multifidelity Bayesian Optimization for Binomial Output Leonid Matyushin • Alexey Zaytsev • Oleg Alenkin • Andrey Ustuzhanin	2019-02-19
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee • Lechao Xiao • Samuel S. Schoenholz • Yasaman Bahri • Roman Novak • Jascha Sohl-Dickstein • Jeffrey Pennington	2019-02-18
The Kalai-Smorodinski solution for many-objective Bayesian optimization Mickaël Binois • Victor Picheny • Patrick Taillandier • Abderrahmane Habbal	2019-02-18
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification Yue Xu • Feng Yin • Wenjun Xu • Jiaru Lin • Shuguang Cui	2019-02-13
Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian Process Behavior, Gradient Independence, and Neural Tangent Kernel Derivation Greg Yang	2019-02-13
Learning interpretable continuous-time models of latent stochastic dynamical systems Lea Duncker • Gergo Bohner • Julien Boussard • Maneesh Sahani	2019-02-12
Multi-objective Bayesian optimisation with preferences over objectives Majid Abdolshah • Alistair Shilton • Santu Rana • Sunil Gupta • Svetha Venkatesh	2019-02-12
Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization Bin Liu	2019-02-11
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping \| Michael Moor • Max Horn • Bastian Rieck • Damian Roqueiro • Karsten Borgwardt	2019-02-05
Combinatorial Bayesian Optimization using the Graph Cartesian Product \| Changyong Oh • Jakub M. Tomczak • Efstratios Gavves • Max Welling	2019-02-01
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach Raed Kontar • Garvesh Raskutti • Shiyu Zhou	2019-01-31
End-to-End Probabilistic Inference for Nonstationary Audio Analysis \| William J. Wilkinson • Michael Riis Andersen • Joshua D. Reiss • Dan Stowell • Arno Solin	2019-01-31
Geometric fluid approximation for general continuous-time Markov chains Michalis Michaelides • Jane Hillston • Guido Sanguinetti	2019-01-31
Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias • Jonathan Schwarz • Alexander G. de G. Matthews • Razvan Pascanu • Yee Whye Teh	2019-01-31
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis Kohei Hayashi • Masaaki Imaizumi • Yuichi Yoshida	2019-01-28
Meta-Learning Mean Functions for Gaussian Processes Vincent Fortuin • Heiko Strathmann • Gunnar Rätsch	2019-01-23
Active Learning with Gaussian Processes for High Throughput Phenotyping \| Sumit Kumar • Wenhao Luo • George Kantor • Katia Sycara	2019-01-21
Modeling and inference of spatio-temporal protein dynamics across brain networks Sara Garbarino • Marco Lorenzi	2019-01-18
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models Shiwei Lan	2019-01-13
Gaussian processes with linear operator inequality constraints Christian Agrell	2019-01-10
No-Regret Bayesian Optimization with Unknown Hyperparameters Felix Berkenkamp • Angela P. Schoellig • Andreas Krause	2019-01-10
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models Randy Ardywibowo • Guang Zhao • Zhangyang Wang • Bobak Mortazavi • Shuai Huang • Xiaoning Qian	2019-01-08
Variational bridge constructs for approximate Gaussian process regression Wil O C Ward • Mauricio A Álvarez	2019-01-07
Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts Andrea Marziali • Emanuele Fabbiani • Giuseppe De Nicolao	2019-01-04
Approximate Inference for Multiplicative Latent Force Models Daniel J. Tait • Bruce J. Worton	2018-12-31
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language \| Jamie Fairbrother • Christopher Nemeth • Maxime Rischard • Johanni Brea • Thomas Pinder	2018-12-21
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation \| Xiaoliang Dai • Peizhao Zhang • Bichen Wu • Hongxu Yin • Fei Sun • Yanghan Wang • Marat Dukhan • Yunqing Hu • Yiming Wu • Yangqing Jia • Peter Vajda • Matt Uyttendaele • Niraj K. Jha	2018-12-21
Towards an Evolvable Cancer Treatment Simulator Richard J. Preen • Larry Bull • Andrew Adamatzky	2018-12-19
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion Seungjoon Lee • Felix Dietrich • George E. Karniadakis • Ioannis G. Kevrekidis	2018-12-16
Efficient Model-Free Reinforcement Learning Using Gaussian Process \| Ying Fan • Letian Chen • Yizhou Wang	2018-12-11
Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest Keir K. Rogers • Hiranya V. Peiris • Andrew Pontzen • Simeon Bird • Licia Verde • Andreu Font-Ribera	2018-12-11
The Limitations of Model Uncertainty in Adversarial Settings Kathrin Grosse • David Pfaff • Michael Thomas Smith • Michael Backes	2018-12-06
Safe Active Learning for Time-Series Modeling with Gaussian Processes Christoph Zimmer • Mona Meister • Duy Nguyen-Tuong	2018-12-01
Learning a latent manifold of odor representations from neural responses in piriform cortex Anqi Wu • Stan Pashkovski • Sandeep R. Datta • Jonathan W. Pillow	2018-12-01
A Bayes-Sard Cubature Method Toni Karvonen • Chris J. Oates • Simo Sarkka	2018-12-01
Scalable Hyperparameter Transfer Learning Valerio Perrone • Rodolphe Jenatton • Matthias W. Seeger • Cedric Archambeau	2018-12-01
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments Mahdi Imani • Seyede Fatemeh Ghoreishi • Ulisses M. Braga-Neto	2018-12-01
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes Rico Angell • Daniel R. Sheldon	2018-12-01
Temporal alignment and latent Gaussian process factor inference in population spike trains Lea Duncker • Maneesh Sahani	2018-12-01
Deep Factors with Gaussian Processes for Forecasting Danielle C. Maddix • Yuyang Wang • Alex Smola	2018-11-30
Neural Non-Stationary Spectral Kernel \| Sami Remes • Markus Heinonen • Samuel Kaski	2018-11-27
Partitioned Variational Inference: A unified framework encompassing federated and continual learning Thang D. Bui • Cuong V. Nguyen • Siddharth Swaroop • Richard E. Turner	2018-11-27
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence Xiu Yang • David Barajas-Solano • Guzel Tartakovsky • Alexandre Tartakovsky	2018-11-24
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior \| Zi Wang • Beomjoon Kim • Leslie Pack Kaelbling	2018-11-23
Mixed Likelihood Gaussian Process Latent Variable Model Samuel Murray • Hedvig Kjellström	2018-11-19
Recursive Sparse Pseudo-input Gaussian Process SARSA John Martin • Brendan Englot	2018-11-17
Mean Square Prediction Error of Misspecified Gaussian Process Models Thomas Beckers • Jonas Umlauft • Sandra Hirche	2018-11-16
Reachability-based safe learning for optimal control problem Stanislav Fedorov • Antonio Candelieri	2018-11-09
Unifying Probabilistic Models for Time-Frequency Analysis \| William J. Wilkinson • Michael Riis Andersen • Joshua D. Reiss • Dan Stowell • Arno Solin	2018-11-06
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu • Yew-Soon Ong • Jianfei Cai	2018-11-03
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu • Jianfei Cai • Yew-Soon Ong • Yi Wang	2018-11-03
Multiplicative Latent Force Models Daniel J. Tait • Bruce J. Worton	2018-11-01
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models Ulrich Paquet • Marco Fraccaro	2018-10-28
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands • Daniel B. Neill • Hannes Nickisch • Andrew Gordon Wilson	2018-10-28
Gaussian Process Prior Variational Autoencoders \| Francesco Paolo Casale • Adrian V Dalca • Luca Saglietti • Jennifer Listgarten • Nicolo Fusi	2018-10-28
Adversarially Robust Optimization with Gaussian Processes Ilija Bogunovic • Jonathan Scarlett • Stefanie Jegelka • Volkan Cevher	2018-10-25
Data Association with Gaussian Processes Markus Kaiser • Clemens Otte • Thomas Runkler • Carl Henrik Ek	2018-10-16
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models Kaspar Märtens • Kieran R. Campbell • Christopher Yau	2018-10-16
Hyperparameter Learning via Distributional Transfer \| Ho Chung Leon Law • Peilin Zhao • Lucian Chan • Junzhou Huang • Dino Sejdinovic	2018-10-15
Non-linear process convolutions for multi-output Gaussian processes Mauricio A. Álvarez • Wil O. C. Ward • Cristian Guarnizo	2018-10-10
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Filip Tronarp • Hans Kersting • Simo Särkkä • Philipp Hennig	2018-10-08
Deep convolutional Gaussian processes \| Kenneth Blomqvist • Samuel Kaski • Markus Heinonen	2018-10-06
GPdoemd: a Python package for design of experiments for model discrimination \| Simon Olofsson • Lukas Hebing • Sebastian Niedenführ • Marc Peter Deisenroth • Ruth Misener	2018-10-05
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks Juho Lee • Yoonho Lee • Jungtaek Kim • Adam R. Kosiorek • Seungjin Choi • Yee Whye Teh	2018-10-01
Adaptive Gaussian process surrogates for Bayesian inference Timur Takhtaganov • Juliane Müller	2018-09-27
High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants Vijay Varma • Davide Gerosa • François Hébert • Leo C. Stein • Hao Zhang	2018-09-24
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models \| Shoubo Hu • Zhitang Chen • Vahid Partovi Nia • Laiwan Chan • Yanhui Geng	2018-09-23
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities Yusuke Tanaka • Tomoharu Iwata • Toshiyuki Tanaka • Takeshi Kurashima • Maya Okawa • Hiroyuki Toda	2018-09-21
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics \| Young-Jin Park • Han-Lim Choi	2018-09-19
A Generalized Representer Theorem for Hilbert Space - Valued Functions Sanket Diwale • Colin Jones	2018-09-19
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze Diego Romeres • Devesh Jha • Alberto Dalla Libera • William Yerazunis • Daniel Nikovski	2018-09-13
Gaussian process classification using posterior linearisation Ángel F. García-Fernández • Filip Tronarp • Simo Särkkä	2018-09-13
Gaussian Process Classification for Variable Fidelity Data \| Nikita Klyuchnikov • Evgeny Burnaev	2018-09-13
Probabilistic approach to limited-data computed tomography reconstruction Zenith Purisha • Carl Jidling • Niklas Wahlström • Simo Särkkä • Thomas B. Schön	2018-09-11
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence Xiu Yang • Guzel Tartakovsky • Alexandre Tartakovsky	2018-09-10
Gaussian Process Regression for Binned Data Michael Thomas Smith • Mauricio A Alvarez • Neil D Lawrence	2018-09-06
Bayesian Nonparametric Spectral Estimation \| Felipe Tobar	2018-09-06
A Multi-layer Gaussian Process for Motor Symptom Estimation in People with Parkinson's Disease Muriel Lang • Franz M. J. Pfister • Jakob Fröhner • Kian Abedinpour • Daniel Pichler • Urban Fietzek • Terry T. Um • Dana Kulić • Satoshi Endo • Sandra Hirche	2018-08-31
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female \| Dewan Fayzur	2018-08-26
Continuous time Gaussian process dynamical models in gene regulatory network inference Atte Aalto • Lauri Viitasaari • Pauliina Ilmonen • Laurent Mombaerts • Jorge Goncalves	2018-08-24
Deep Convolutional Networks as shallow Gaussian Processes \| Adrià Garriga-Alonso • Carl Edward Rasmussen • Laurence Aitchison	2018-08-16
Locally-adaptive Bayesian nonparametric inference for phylodynamics James R. Faulkner • Andrew F. Magee • Beth Shapiro • Vladimir N. Minin	2018-08-13
Exploiting Structure for Fast Kernel Learning \| Trefor W. Evans • Prasanth B. Nair	2018-08-09
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting Stephanie Tan • Hayley Hung	2018-08-06
Machine Learning of Space-Fractional Differential Equations Mamikon Gulian • Maziar Raissi • Paris Perdikaris • George Karniadakis	2018-08-02
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes Christian Donner • Manfred Opper	2018-08-02
A Data-Efficient Approach to Precise and Controlled Pushing Maria Bauza • Francois R. Hogan • Alberto Rodriguez	2018-07-26
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization Zhaozhong Chen • Christoffer Heckman • Simon Julier • Nisar Ahmed	2018-07-23
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda • Katherine Driggs-Campbell • Mykel J. Kochenderfer	2018-07-22
Deep Learning for Epidemiological Predictions Wu • Yuexin Yang • Yiming Nishiura • Hiroshi Saitoh • Masaya	2018-07-21
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation Xiong Lyu • Mickael Binois • Michael Ludkovski	2018-07-18
Battery health prediction under generalized conditions using a Gaussian process transition model Robert R. Richardson • Michael A. Osborne • David A. Howey	2018-07-17
Machine Learning of Energetic Material Properties Brian C. Barnes • Daniel C. Elton • Zois Boukouvalas • DeCarlos E. Taylor • William D. Mattson • Mark D. Fuge • Peter W. Chung	2018-07-17
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors Danil Kuzin • Olga Isupova • Lyudmila Mihaylova	2018-07-15
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures Andrew R. Lawrence • Carl Henrik Ek • Neill D. F. Campbell	2018-07-12
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion \| Steven Atkinson • Nicholas Zabaras	2018-07-11
An Empirical Approach For Probing the Definiteness of Kernels Martin Zaefferer • Thomas Bartz-Beielstein • Günter Rudolph	2018-07-10
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning Danil Kuzin • Le Yang • Olga Isupova • Lyudmila Mihaylova	2018-07-09
A Tutorial on Bayesian Optimization \| Peter I. Frazier	2018-07-08
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences