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 TanNiv NaymanMengchang WangRong Jin
2021-01-13
Automated feature selection for data-driven models of rapid battery capacity fade and end of life
Samuel GreenbankDavid A. Howey
2021-01-12
Regret Analysis of Distributed Gaussian Process Estimation and Coverage
Lai WeiAndrew McDonaldVaibhav Srivastava
2021-01-12
An asymptotic formula for the variance of the number of zeroes of a stationary Gaussian process
Eran AssafJeremiah BuckleyNaomi Feldheim
2021-01-11
Fast calculation of Gaussian Process multiple-fold cross-validation residuals and their covariances
David GinsbourgerCedric Schärer
2021-01-08
Retrieval of Coloured Dissolved Organic Matter with Machine Learning Methods
Ana B. RuescasMartin HieronymiSampsa KoponenKari KallioGustau Camps-Valls
2021-01-07
Modeling massive multivariate spatial data with the basis graphical lasso
Mitchell KrockWilliam KleiberDorit HammerlingStephen Becker
2021-01-07
Infinitely Wide Tensor Networks as Gaussian Process
Erdong GuoDavid Draper
2021-01-07
Multi-instrumental view of magnetic fields and activity of $ε$ Eridani with SPIRou, NARVAL, and TESS
P. PetitC. P. FolsomJ. -F. DonatiL. YuJ. -D. do Nascimento Jr.S. JeffersS. C. MarsdenJ. MorinA. A. Vidotto
2021-01-07
Gaussian Function On Response Surface Estimation
Mohammadhossein ToutiaeeJohn Miller
2021-01-04
Gauss-Legendre Features for Gaussian Process Regression
Paz Fink ShustinHaim Avron
2021-01-04
Phase Transitions in Recovery of Structured Signals from Corrupted Measurements
Zhongxing SunWei CuiYulong 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 LiuXiaosong HuZhongbao WeiYi LiYan Jiang
2020-12-31
Particle Swarm Based Hyper-Parameter Optimization for Machine Learned Interatomic Potentials
Suresh Kondati NatarajanMiguel A. Caro
2020-12-31
Social media data reveals signal for public consumer perceptions
Neeti PokhriyalAbenezer DaraBenjamin ValentinoSoroush Vosoughi
2020-12-26
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Suman MajumderYawen GuanBrian J. ReichArvind K. Saibaba
2020-12-24
Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions
Sumit JhaCarlos Busso
2020-12-23
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations
Jialei ChenZhehui ChenChuck ZhangC. F. Jeff Wu
2020-12-22
Gaussian Process Regression constrained by Boundary Value Problems
Mamikon GulianAri FrankelLaura Swiler
2020-12-22
Having a Ball: evaluating scoring streaks and game excitement using in-match trend estimation
Claus Thorn EkstrømAndreas Kryger Jensen
2020-12-22
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior
Anh TongToan TranHung BuiJaesik Choi
2020-12-21
A Bayesian methodology for localising acoustic emission sources in complex structures
Matthew R. JonesTim J. RogersKeith WordenElizabeth J. Cross
2020-12-21
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach
Yvonne R. StürzMohammad KhosraviRoy S. Smith
2020-12-20
Guiding Neural Network Initialization via Marginal Likelihood Maximization
Anthony S. TaiChunfeng Huang
2020-12-17
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach
MohammadNoor InjadatAbdallah MoubayedAbdallah Shami
2020-12-16
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
Anna Mateo-SanchisJordi Munoz-MariManuel Campos-TabernerJavier Garcia-HaroGustau Camps-Valls
2020-12-11
Exact Bayesian inference for level-set Cox processes
Flavio B. GonçalvesBarbara C. C. Dias
2020-12-10
On the Environmental Variability of Offshore Wind Power
Behzad GolparvarPetros PapadopoulosAhmed Aziz EzzatRuo-Qian Wang
2020-12-08
Retrieval of Case 2 Water Quality Parameters with Machine Learning
Ana B. RuescasGonzalo Mateo-GarciaGustau Camps-VallsMartin Hieronymi
2020-12-08
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
Katja BergerJochem VerrelstJean-Baptiste FéretTobias HankMatthias WocherWolfram MauserGustau Camps-Valls
2020-12-07
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Taehyeon KimJaeyeon AhnNakyil KimSeyoung Yun
2020-12-07
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
YiFan LiChao LiStephen PriceCarola-Bibiane SchönliebXi Chen
2020-12-05
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes
Luca PipiaJordi Muñoz-MaríEatidal AminSantiago BeldaGustau Camps-VallsJochem Verrelst
2020-12-05
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
Giulio OrtaliNicola DemoGianluigi Rozza
2020-12-03
A similarity-based Bayesian mixture-of-experts model
Tianfang ZhangRasmus BokrantzJimmy Olsson
2020-12-03
Gaussian Process-based Approach for Bilevel Optimization in Power Systems -- A Critical Load Restoration Case
Yang LiuHung D. Nguyen
2020-12-02
A Gaussian Process-based Price-Amount Curve Construction for Demand Response Provided by Internet Data Centers
Yang LiuHung D. Nguyen
2020-12-02
The temporal overfitting problem with applications in wind power curve modeling
Abhinav PrakashRui TuoYu Ding
2020-12-02
Gaussian Process Based Message Filtering for Robust Multi-Agent Cooperation in the Presence of Adversarial Communication
Rupert MitchellJan BlumenkampAmanda Prorok
2020-12-01
Identifying signal and noise structure in neural population activity with Gaussian process factor models
Stephen KeeleyMikio AoiYiyi YuSpencer SmithJonathan W. Pillow
2020-12-01
Non-reversible Gaussian processes for identifying latent dynamical structure in neural data
Virginia RuttenAlberto BernacchiaManeesh SahaniGuillaume 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 ChenLili ZhengRaed Al KontarGarvesh Raskutti
2020-12-01
Generalised Bayesian Filtering via Sequential Monte Carlo
Ayman BoustatiOmer Deniz AkyildizTheodoros DamoulasAdam Johansen
2020-12-01
An emulator for the Lyman-$α$ forest in beyond-$Λ$CDM cosmologies
Christian PedersenAndreu Font-RiberaKeir K. RogersPatrick McDonaldHiranya V. PeirisAndrew PontzenAnže Slosar
2020-11-30
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes
Morgane MenzSylvain DubreuilJérôme MorioChristian GoguNathalie BartoliMarie Chiron
2020-11-30
Equivalence of Convergence Rates of Posterior Distributions and Bayes Estimators for Functions and Nonparametric Functionals
Zejian LiuMeng Li
2020-11-27
Knowledge transfer across cell lines using Hybrid Gaussian Process models with entity embedding vectors
Clemens HutterMoritz von StoschMariano Nicolas Cruz BournazouAlessandro Butté
2020-11-27
Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytope
Jungtaek KimMinsu ChoSeungjin 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. VasudevanKyle KelleyHiroshi FunakuboStephen JesseSergei V. KalininMaxim Ziatdinov
2020-11-25
Equivariant Conditional Neural Processes
Peter HolderriethMichael HutchinsonYee 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 RenMohamed Ali BoussaidiDmitry VoytsekhovskySergei 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 GuinetValerio PeronneCédric Archambeau
2020-11-23
Robust Gaussian Process Regression Based on Iterative Trimming
Zhao-Zhou LiLu LiZhengyi Shao
2020-11-22
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Cheolhei LeeJianguo WuWenjia WangXiaowei Yue
2020-11-21
Central and Non-central Limit Theorems arising from the Scattering Transform and its Neural Activation Generalization
Gi-Ren LiuYuan-Chung SheuHau-Tieng Wu
2020-11-21
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry
Yizhou QianMojtaba ForghaniJonghyun Harry LeeMatthew FarthingTyler HesserPeter KitanidisEric Darve
2020-11-19
Learning Interpretable Flight's 4D Landing Parameters Using Tunnel Gaussian Process
Sim Kuan GohNarendra Pratap SinghZhi Jun LimSameer Alam
2020-11-18
Understanding Variational Inference in Function-Space
David R. BurtSebastian W. OberAdrià Garriga-AlonsoMark van der Wilk
2020-11-18
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects
Fernando CastañedaJason J. ChoiBike ZhangClaire J. TomlinKoushil Sreenath
2020-11-14
Towards Human-Level Learning of Complex Physical Puzzles
Kei OtaDevesh K. JhaDiego RomeresJeroen van BaarKevin A. SmithTakayuki SemitsuTomoaki OikiAlan SullivanDaniel NikovskiJoshua B. Tenenbaum
2020-11-14
Factorized Gaussian Process Variational Autoencoders
| Metod JazbecMichael PearceVincent Fortuin
2020-11-14
Towards NNGP-guided Neural Architecture Search
Daniel S. ParkJaehoon LeeDaiyi PengYuan CaoJascha Sohl-Dickstein
2020-11-11
Energy consumption forecasting using a stacked nonparametric Bayesian approach
Dilusha WeeraddanaNguyen Lu Dang KhoaLachlan O NeilWeihong WangChen Cai
2020-11-11
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
Erik DrysdaleDevin SinghAnna Goldenberg
2020-11-09
Pathwise Conditioning of Gaussian Processes
James T. WilsonViacheslav BorovitskiyAlexander TereninPeter MostowskyMarc Peter Deisenroth
2020-11-08
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
Chaoqi WangShengyang SunRoger Grosse
2020-11-06
Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders
Rohit BatraHanjun DaiTran Doan HuanLihua ChenChiho KimWill R. GutekunstLe SongRampi Ramprasad
2020-11-04
Bayesian Variational Optimization for Combinatorial Spaces
Tony C. WuDaniel Flam-ShepherdAlán Aspuru-Guzik
2020-11-03
Uncertainty Quantification of Darcy Flow through Porous Media using Deep Gaussian Process
A. DaneshkhahM. Mousavi NezhadO. ChatrabgounM. EsmaeilbeigiT. SedighiS. Abolfathi
2020-11-03
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix
Julian KrebsHervé DelingetteNicholas AyacheTommaso Mansi
2020-11-03
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Xingyu LeiStudent MemberZhifang YangMemberJunbo ZhaoJuan YuSenior MemberIEEE
2020-11-02
Sample-efficient reinforcement learning using deep Gaussian processes
Charles GaddMarkus HeinonenHarri LähdesmäkiSamuel Kaski
2020-11-02
Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks
Zoe L. de BeursAndrew VanderburgChristopher J. ShallueXavier DumusqueAndrew Collier CameronLars A. BuchhaveRosario CosentinoAdriano GhedinaRaphaëlle D. HaywoodNicholas LangellierDavid W. LathamMercedes López-MoralesMichel MayorGiusi MicelaTimothy W. MilbourneAnnelies MortierEmilio MolinariFrancesco PepeDavid F. PhillipsMatteo PinamontiGiampaolo PiottoKen RiceDimitar SasselovAlessandro SozzettiStéphane UdryChristopher A. Watson
2020-10-30
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
| Vu NguyenVaden MasraniRob BrekelmansMichael A. OsborneFrank Wood
2020-10-29
Matern Gaussian Processes on Graphs
Viacheslav BorovitskiyIskander AzangulovAlexander TereninPeter MostowskyMarc Peter DeisenrothNicolas Durrande
2020-10-29
A Computationally Efficient Approach to Black-box Optimization using Gaussian Process Models
Sudeep SalgiaSattar VakiliQing Zhao
2020-10-27
Are wider nets better given the same number of parameters?
Anna GolubevaBehnam NeyshaburGuy Gur-Ari
2020-10-27
Scalable Gaussian Process Variational Autoencoders
| Metod JazbecVincent FortuinMichael PearceStephan MandtGunnar Rätsch
2020-10-26
Variational Bayesian Unlearning
Quoc Phong NguyenBryan Kian Hsiang LowPatrick Jaillet
2020-10-24
Ranking Creative Language Characteristics in Small Data Scenarios
Julia SiekieraMarius KöppelEdwin SimpsonKevin StoweIryna GurevychStefan Kramer
2020-10-23
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
Sean PlummerShuang ZhouAnirban BhattacharyaDavid DunsonDebdeep Pati
2020-10-23
Sparse Gaussian Process Variational Autoencoders
Matthew AshmanJonathan SoWilliam TebbuttVincent FortuinMichael PearceRichard E. Turner
2020-10-20
Federated Bayesian Optimization via Thompson Sampling
Zhongxiang DaiKian Hsiang LowPatrick Jaillet
2020-10-20
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi MeronenChristabella IrwantoArno Solin
2020-10-19
Movement-induced Priors for Deep Stereo
Yuxin HouMuhammad Kamran JanjuaJuho KannalaArno Solin
2020-10-18
KrigHedge: GP Surrogates for Delta Hedging
Mike LudkovskiYuri Saporito
2020-10-16
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Francesco RomorMarco TezzeleGianluigi Rozza
2020-10-16
Diffusion Based Gaussian Processes on Restricted Domains
David B DunsonHau-Tieng WuNan Wu
2020-10-14
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit
Ben AdlamJaehoon LeeLechao XiaoJeffrey PenningtonJasper Snoek
2020-10-14
Local Differential Privacy for Bayesian Optimization
Xingyu ZhouJian Tan
2020-10-13
Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
Sebastian HaanFabio RamosDietmar Müller
2020-10-12
Online Learning and Distributed Control for Residential Demand Response
Xin ChenYingYing LiJun ShimadaNa Li
2020-10-11
Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids
Tong MaDavid Alonso Barajas-SolanoRamakrishna TipireddyAlexandre M. Tartakovsky
2020-10-09
Dynamic mode decomposition for forecasting and analysis of power grid load data
Daniel DylewskyDavid Barajas-SolanoTong MaAlexandre M. TartakovskyJ. Nathan Kutz
2020-10-08
Emulator-based global sensitivity analysis for flow-like landslide run-out models
Hu ZhaoFlorian AmannJulia Kowalski
2020-10-08
Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
| Mona FuhrländerSebastian Schöps
2020-10-08
Bayesian Optimized Monte Carlo Planning
John MernAnil YildizZachary SunbergTapan MukerjiMykel J. Kochenderfer
2020-10-07
Splitting Gaussian Process Regression for Streaming Data
Nick TerryYoungjun Choe
2020-10-06
Gaussian Process Models with Low-Rank Correlation Matrices for Both Continuous and Categorical Inputs
Dominik KirchhoffSonja Kuhnt
2020-10-06
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features
Agustinus KristiadiMatthias HeinPhilipp Hennig
2020-10-06
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
Casey StowersTaeksang LeeIlias BilionisArun GosainAdrian Buganza Tepole
2020-10-05
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models
Paul WestermannRalph Evins
2020-10-05
Short-term prediction of photovoltaic power generation using Gaussian process regression
Yahya Al LawatiJack KellyDan Stowell
2020-10-05
BOSS: Bayesian Optimization over String Spaces
Henry B. MossDaniel BeckJavier GonzalezDavid S. LesliePaul Rayson
2020-10-02
Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets
Banus JaumeSermesant MaximeCamara OscarLorenzi Marco
2020-10-02
Gravitational wave peak luminosity model for precessing binary black holes
Afura TaylorVijay Varma
2020-09-30
Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering
Joris GuerinAnne Magaly de Paula CanutoLuiz Marcos Garcia Goncalves
2020-09-29
Multi-task Causal Learning with Gaussian Processes
Virginia AgliettiTheodoros DamoulasMauricio ÁlvarezJavier González
2020-09-27
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems
Bruno Henrique Groenner BarbosaNan XuHassan AskariAmir Khajepour
2020-09-25
Stein Variational Gaussian Processes
Thomas PinderChristopher NemethDavid Leslie
2020-09-25
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization
Ehecatl Antonio del Rio-ChanonaPanagiotis PetsagkourakisEric BradfordJose Eduardo Alves GracianoBenoit 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 IwazakiYu InatsuIchiro Takeuchi
2020-09-17
Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments
Xi ChenYu GuanJian-Qing ShiXiu-Li DuJanet Eyre
2020-09-17
Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes
Shihao YangSamuel W. K. WongS. C. Kou
2020-09-16
On Information Gain and Regret Bounds in Gaussian Process Bandits
Sattar VakiliKia KhezeliVictor Picheny
2020-09-15
Interpolating the Trace of the Inverse of Matrix $\mathbf{A} + t \mathbf{B}$
Siavash AmeliShawn C. Shadden
2020-09-15
Tracking disease outbreaks from sparse data with Bayesian inference
Bryan WilderMichael J. MinaMilind Tambe
2020-09-12
Symplectic Gaussian Process Regression of Hamiltonian Flow Maps
Katharina RathChristopher G. AlbertBernd BischlUdo von Toussaint
2020-09-11
Generalized Multi-Output Gaussian Process Censored Regression
Daniele GammelliKasper Pryds RolstedDario PacinoFilipe Rodrigues
2020-09-10
A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands
R. G. SeymourT. KypraiosP. D. O'NeillT. J. Hagenaars
2020-09-09
$\mathcal{RL}_1$-$\mathcal{GP}$: Safe Simultaneous Learning and Control
Aditya GahlawatArun LakshmananLin SongAndrew PattersonZhuohuan WuNaira HovakimyanEvangelos Theodorou
2020-09-08
Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People
Maxime De BoisMounîm A. El YacoubiMehdi Ammi
2020-09-08
Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
Adi HanukaX. HuangJ. ShtalenkovaD. KennedyA. EdelenV. R. LalchandD. RatnerJ. Duris
2020-09-08
Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition
Alistair ShiltonSunil GuptaSantu RanaSvetha Venkatesh
2020-09-08
Augmented Gaussian Random Field: Theory and Computation
Sheng ZhangXiu YangSamy TindelGuang Lin
2020-09-03
Real Image Super Resolution Via Heterogeneous Model using GP-NAS
Zhihong PanBaopu LiTeng XiYanwen FanGang ZhangJingtuo LiuJunyu HanErrui Ding
2020-09-02
Non-parametric generalized linear model
Matthew DowlingYuan ZhaoIl Memming Park
2020-09-02
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
Cedric Le GentilMallikarjuna VayugundlaRiccardo GiubilatoWolfgang StürzlTeresa Vidal-CallejaRudolph Triebel
2020-09-01
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
| Haitao LiuYew-Soon OngXiaomo JiangXiaofang Wang
2020-08-29
Machine learning thermal circuit network model for thermal design optimization of electronic circuit board layout with transient heating chips
Daiki OtakiHirofumi NonakaNoboru Yamada
2020-08-28
Fast Bayesian Force Fields from Active Learning: Study of Inter-Dimensional Transformation of Stanene
| Yu XieJonathan VandermauseLixin SunAndrea CepellottiBoris Kozinsky
2020-08-26
Variable selection for Gaussian process regression through a sparse projection
Chiwoo ParkDavid J. BorthNicholas S. WilsonChad N. Hunter
2020-08-25
Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
David J. ArmstrongJevgenij GamperTheodoros Damoulas
2020-08-24
Fast Approximate Multi-output Gaussian Processes
| Vladimir JoukovDana Kulić
2020-08-22
On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization
Xu CaiJonathan Scarlett
2020-08-20
Improving predictions of Bayesian neural networks via local linearization
Alexander ImmerMaciej KorzepaMatthias Bauer
2020-08-19
Bayesian neural networks and dimensionality reduction
Deborshee SenTheodore PapamarkouDavid Dunson
2020-08-18
Preferential Bayesian optimisation with Skew Gaussian Processes
Alessio BenavoliDario AzzimontiDario Piga
2020-08-15
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
Waad SubberSayan GhoshPiyush PanditaYiming ZhangLiping Wang
2020-08-14
Continuous Optimization Benchmarks by Simulation
| Martin ZaeffererFrederik Rehbach
2020-08-14
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Elena ArcariAndrea CarronMelanie N. Zeilinger
2020-08-13
Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP
Sungho YoonAyoung Kim
2020-08-12
Deep State-Space Gaussian Processes
Zheng ZhaoMuhammad EmzirSimo Särkkä
2020-08-11
Multi-speaker Text-to-speech Synthesis Using Deep Gaussian Processes
Kentaro MitsuiTomoki KoriyamaHiroshi Saruwatari
2020-08-07
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression
Yixiang DengGuang LinXiu Yang
2020-08-03
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships
Nina KudryashovaTheoklitos AmvrosiadisNathalie DupuyNathalie RochefortArno Onken
2020-08-03
OFAI-UKP at [email protected]: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
Tristan MillerErik-Lân Do DinhEdwin SimpsonIryna Gurevych
2020-08-03
Finite Versus Infinite Neural Networks: an Empirical Study
Jaehoon LeeSamuel S. SchoenholzJeffrey PenningtonBen AdlamLechao XiaoRoman NovakJascha Sohl-Dickstein
2020-07-31
Cold Posteriors and Aleatoric Uncertainty
Ben AdlamJasper SnoekSamuel L. Smith
2020-07-31
Random Forests for dependent data
Arkajyoti SahaSumanta BasuAbhirup Datta
2020-07-30
Regression modelling with I-priors
Wicher BergsmaHaziq Jamil
2020-07-30
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
| Santiago Toledo-CortésMelissa De La PavaOscar PerdómoFabio A. González
2020-07-29
Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment
| A. F. López-LoperaD. IdierJ. RohmerF. Bachoc
2020-07-28
Bayesian Dynamic Mapping of an Exo-Earth from Photometric Variability
| Hajime KawaharaKento Masuda
2020-07-26
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
Romit MaulikThemistoklis BotsasNesar RamachandraLachlan Robert MasonIndranil Pan
2020-07-23
MAGMA: Inference and Prediction with Multi-Task Gaussian Processes
Arthur LeroyPierre LatoucheBenjamin GuedjServane 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 RibeiroRamon Gomes da SilvaViviana Cocco MarianiLeandro dos Santos Coelho
2020-07-21
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization
Yang YangKe DengMichael Zhu
2020-07-20
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Jake SnellRichard Zemel
2020-07-20
Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach
Yun YuanQinzheng WangXianfeng 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 YuanZhao ZhangXianfeng Terry Yang
2020-07-14
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
| William J. WilkinsonPaul E. ChangMichael Riis AndersenArno Solin
2020-07-12
Driver Behavior Modelling at the Urban Intersection via Canonical Correlation Analysis
Zirui LiChao LuCheng GongCheng GongJinghang LiLianzhen Wei
2020-07-11
Numerical simulation, clustering and prediction of multi-component polymer precipitation
Pavan InguvaLachlan MasonIndranil PanMiselle HengardiOmar K. Matar
2020-07-10
Inferring proximity from Bluetooth Low Energy RSSI with Unscented Kalman Smoothers
Tom LovettMark BriersMarcos CharalambidesRadka JersakovaJames LomaxChris Holmes
2020-07-09
Understanding the effect of hyperparameter optimization on machine learning models for structure design problems
Xianping DuHongyi XuFeng Zhu
2020-07-04
Gaussian Process Regression with Local Explanation
Yuya YoshikawaTomoharu Iwata
2020-07-03
BOSH: Bayesian Optimization by Sampling Hierarchically
Henry B. MossDavid S. LesliePaul Rayson
2020-07-02
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Elena RaponiHao WangMariusz BujnySimonetta BoriaCarola Doerr
2020-07-02
Wearable Respiration Monitoring: Interpretable Inference with Context and Sensor Biomarkers
Ridwan AlamDavid B. PedenJohn C. Lach
2020-07-02
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes
Ali HebbalLoic BrevaultMathieu BalesdentEl-Ghazali TalbiNouredine Melab
2020-06-29
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali JiangDaniel R. JiangMaximilian BalandatBrian KarrerJacob R. GardnerRoman Garnett
2020-06-29
Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process
Liwei WangSiyu TaoPing ZhuWei Chen
2020-06-27
Prediction with Gaussian Process Dynamical Models
Thomas BeckersSandra Hirche
2020-06-25
Epoch-evolving Gaussian Process Guided Learning
Jiabao CuiXuewei LiBin LiHanbin ZhaoBourahla OmarXi Li
2020-06-25
Heat kernel and intrinsic Gaussian processes on manifolds
Ke YeMu NiuPokman Cheung
2020-06-25
Green Machine Learning via Augmented Gaussian Processes and Multi-Information Source Optimization
Antonio CandelieriRiccardo PeregoFrancesco 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 NikaKerem BozganÇağın AraratCem Tekin
2020-06-24
Variational Orthogonal Features
David R. BurtCarl Edward RasmussenMark van der Wilk
2020-06-23
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
| Yuling YaoAki VehtariAndrew Gelman
2020-06-22
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure
Shogo IwazakiYu InatsuIchiro Takeuchi
2020-06-22
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
| Mengdi XuWenhao DingJiacheng ZhuZuxin LiuBaiming ChenDing Zhao
2020-06-19
Likelihood-Free Inference with Deep Gaussian Processes
Alexander AushevHenri PesonenMarkus HeinonenJukka CoranderSamuel Kaski
2020-06-18
Exact posterior distributions of wide Bayesian neural networks
Jiri HronYasaman BahriRoman NovakJeffrey PenningtonJascha Sohl-Dickstein
2020-06-18
GPIRT: A Gaussian Process Model for Item Response Theory
JBrandon Duck-MayrRoman GarnettJacob M. Montgomery
2020-06-17
Longitudinal Variational Autoencoder
Siddharth RamchandranGleb TikhonovMiika KoskinenHarri Lähdesmäki
2020-06-17
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Jeremiah Zhe LiuZi LinShreyas PadhyDustin TranTania Bedrax-WeissBalaji Lakshminarayanan
2020-06-17
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Laura SwilerMamikon GulianAri FrankelCosmin SaftaJohn Jakeman
2020-06-16
Real-Time Regression with Dividing Local Gaussian Processes
Armin LedererAlejandro Jose Ordonez ConejoKorbinian MaierWenxin XiaoSandra Hirche
2020-06-16
Sparse Gaussian Process Based On Hat Basis Functions
Wenqi FangHuiyun LiHui HuangShaobo DangZhejun HuangZheng Wang
2020-06-15
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Sebastian CuriFelix BerkenkampAndreas 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 LedererMarkus KesslerSandra Hirche
2020-06-14
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Wenxin XiaoArmin LedererSandra Hirche
2020-06-14
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search
Vu NguyenTam LeMakoto YamadaMichael A Osborne
2020-06-13
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel
Binxin RuXingchen WanXiaowen DongMichael Osborne
2020-06-13
Automated Measurement of Quasar Redshift with a Gaussian Process
Leah FauberMing-Feng HoSimeon BirdChristian R. SheltonRoman GarnettIshita Korde
2020-06-12
Gaussian Processes on Graphs via Spectral Kernel Learning
Yin-Cong ZhiYin Cheng NgXiaowen Dong
2020-06-12
Fourier Sparse Leverage Scores and Approximate Kernel Learning
Tamás ErdélyiCameron MuscoChristopher Musco
2020-06-12
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
Kristopher T. JensenTa-Chu KaoMarco TripodiGuillaume Hennequin
2020-06-12
SAMBA: Safe Model-Based & Active Reinforcement Learning
Alexander I. Cowen-RiversDaniel PalenicekVincent MoensMohammed AbdullahAivar SootlaJun WangHaitham Ammar
2020-06-12
Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis
MohammadReza EbrahimiNavona CalarcoKieran CampbellColin HawcoAristotle VoineskosAshish Khisti
2020-06-10
Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model
Tianjian ZhouYuan Ji
2020-06-10
Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam KapoorTheofanis KaraletsosThang D. Bui
2020-06-09
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison ZhuXing LiuRuya KangZhichao ShenSeth FlaxmanFrançois-Xavier Briol
2020-06-09
Scalable Thompson Sampling using Sparse Gaussian Process Models
Sattar VakiliVictor PichenyArtem Artemev
2020-06-09
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder
Alex CampbellPietro Liò
2020-06-08
Schrödinger PCA: You Only Need Variances for Eigenmodes
Ziming LiuSitian QianYixuan WangYuxuan YanTianyi Yang
2020-06-08
Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation
| Julian BerkSunil GuptaSantu RanaSvetha Venkatesh
2020-06-08
Physics Regularized Gaussian Processes
Zheng WangWei XingRobert KirbyShandian Zhe
2020-06-08
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
Zheng WangWei XingRobert KirbyShandian Zhe
2020-06-08
Learning compositional models of robot skills for task and motion planning
Zi WangCaelan Reed GarrettLeslie Pack KaelblingTomás Lozano-Pérez
2020-06-08
A conditional one-output likelihood formulation for multitask Gaussian processes
Vanessa Gómez-VerdejoÓscar García-HindeManel Martínez-Ramón
2020-06-05
Health Indicator Forecasting for Improving Remaining Useful Life Estimation
Qiyao WangAhmed FarahatChetan GuptaHaiyan 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. EvansPrasanth B. Nair
2020-06-04
Automatic Setting of DNN Hyper-Parameters by Mixing Bayesian Optimization and Tuning Rules
Michele FraccaroliEvelina LammaFabrizio Riguzzi
2020-06-03
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels
Marcus M. NoackGregory S. DoerkRuipeng LiJason K. StreitRichard A. VaiaKevin G. YagerMasafumi 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 DhamalaJohn L. SappB. Milan HorácekLinwei Wang
2020-06-02
Semi-supervised deep learning for high-dimensional uncertainty quantification
Zequn WangMingyang 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 CarvalhoFlávio Rainho ÁvilaLuiz Wagner Pereira Biscainho
2020-05-28
Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks
Jinglin ZhangWenjun XuHui GaoMiao PanZhu HanPing Zhang
2020-05-28
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
| Koorosh AslansefatIoannis SorokosDeclan WhitingRamin Tavakoli KolagariYiannis Papadopoulos
2020-05-27
How Training Data Impacts Performance in Learning-based Control
Armin LedererAlexandre CaponeJonas UmlauftSandra Hirche
2020-05-25
Path Imputation Strategies for Signature Models of Irregular Time Series
Michael MoorMax HornChristian BockKarsten BorgwardtBastian Rieck
2020-05-25
Longitudinal Deep Kernel Gaussian Process Regression
Junjie LiangYanting WuDongkuan XuVasant Honavar
2020-05-24
MANGO: A Python Library for Parallel Hyperparameter Tuning
| Sandeep Singh SandhaMohit AggarwalIgor FedorovMani Srivastava
2020-05-22
Consistency of Empirical Bayes And Kernel Flow For Hierarchical Parameter Estimation
Yifan ChenHouman OwhadiAndrew M. Stuart
2020-05-22
Data-driven Efficient Solvers and Predictions of Conformational Transitions for Langevin Dynamics on Manifold in High Dimensions
Yuan GaoJian-Guo LiuNan Wu
2020-05-22
Global Optimization of Gaussian processes
Artur M. SchweidtmannDominik BongartzDaniel GrotheTim KerkenhoffXiaopeng LinJaromil NajmanAlexander Mitsos
2020-05-21
Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide
Kyle P MessierMatthias Katzfuss
2020-05-19
Deep Latent-Variable Kernel Learning
| Haitao LiuYew-Soon OngXiaomo JiangXiaofang Wang
2020-05-18
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
Lai WeiXiaobo TanVaibhav Srivastava
2020-05-18
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
Sebastian W. OberLaurence Aitchison
2020-05-17
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes
Zhaozhi QianAhmed M. AlaaMihaela van der Schaar
2020-05-13
Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
Arrykrishna MootoovalooAlan F. HeavensAndrew H. JaffeFlorent Leclercq
2020-05-13
Machine learning based digital twin for dynamical systems with multiple time-scales
Souvik ChakrabortySondipon Adhikari
2020-05-12
Multi-Scale Zero-Order Optimization of Smooth Functions in an RKHS
Shubhanshu ShekharTara Javidi
2020-05-11
Upper Trust Bound Feasibility Criterion for Mixed Constrained Bayesian Optimization with Application to Aircraft Design
Rémy PriemNathalie BartoliYoussef DiouaneAlessandro Sgueglia
2020-05-11
Multi-Fidelity Gaussian Process based Empirical Potential Development for Si:H Nanowires
Moonseop KimHuayi YinGuang Lin
2020-05-11
Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques
Moajjem Hossain ChowdhuryMd Nazmul Islam ShuzanMuhammad E. H. ChowdhuryZaid B MahbubM. Monir UddinAmith KhandakarMamun Bin Ibne Reaz
2020-05-07
A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
Steven I ReevesDongwook LeeAnurag SinghKunal Verma
2020-05-07
Planning from Images with Deep Latent Gaussian Process Dynamics
Nathanael BoschJan AchterholdLaura Leal-TaixéJörg Stückler
2020-05-07
Active Preference-Based Gaussian Process Regression for Reward Learning
| Erdem BıyıkNicolas HuynhMykel J. KochenderferDorsa Sadigh
2020-05-06
Using Machine Learning to Emulate Agent-Based Simulations
Claudio AngioneEric SilvermanElisabeth Yaneske
2020-05-05
Localized active learning of Gaussian process state space models
Alexandre CaponeJonas UmlauftThomas BeckersArmin LedererSandra Hirche
2020-05-04
Evaluation of Deep Gaussian Processes for Text Classification
P. JayashreeP. K. Srijith
2020-05-01
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition
Raul QuinteroIgnacio ParraDavid Fernandez LlorcaMiguel Angel Sotelo
2020-04-30
Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo PanSiddharth SwaroopAlexander ImmerRuna EschenhagenRichard E. TurnerMohammad 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. KalininMaxim ZiatdinovRama K. Vasudevan
2020-04-27
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage
Xiaowei YueYuchen WenJeffrey H. HuntJianjun Shi
2020-04-23
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck
Alec KoppelHrusikesha PradhanKetan Rajawat
2020-04-23
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
| A. Rene GeistSebastian Trimpe
2020-04-23
Gaussian Process Manifold Interpolation for Probabilistic Atrial Activation Maps and Uncertain Conduction Velocity
Sam CoveneyCesare CorradoCaroline H RoneyDaniel O'HareSteven E WilliamsMark D O'NeillSteven A NiedererRichard H ClaytonJeremy E OakleyRichard D Wilkinson
2020-04-22
Utterance-level Sequential Modeling For Deep Gaussian Process Based Speech Synthesis Using Simple Recurrent Unit
Tomoki KoriyamaHiroshi Saruwatari
2020-04-22
Machine learning for multiple yield curve markets: fast calibration in the Gaussian affine framework
Sandrine GümbelThorsten Schmidt
2020-04-16
Gaussian Process Learning-based Probabilistic Optimal Power Flow
Parikshit PareekHung D. Nguyen
2020-04-16
Foreground modelling via Gaussian process regression: an application to HERA data
Abhik GhoshFlorent MertensGianni BernardiMário G. SantosNicholas S. KernChristopher L. CarilliTrienko L. GroblerLéon V. E. KoopmansDaniel C. JacobsAdrian LiuAaron R. ParsonsMiguel F. MoralesJames E. AguirreJoshua S. DillonBryna J. HazeltonOleg M. SmirnovBharat K. GehlotSiyanda MatikaPaul AlexanderZaki S. AliAdam P. BeardsleyRoshan K. BenefoTashalee S. BillingsJudd D. BowmanRichard F. BradleyCarina ChengPaul M. ChichuraDavid R. DeBoerEloy de Lera AcedoAaron Ewall-WiceGcobisa FadanaNicolas FagnoniAustin F. FortinoRandall FritzSteve R. FurlanettoSamavarti GallardoBrian GlendenningDeepthi GorthiBradley GreigJasper GrobbelaarJack HickishAlec JosaitisAustin JuliusAmy S. IgarashiMacCalvin KarisebSaul A. KohnMatthew KolopanisTelalo LekalakeAnita LootsDavid MacMahonLourence MalanCresshim MalgasMatthys MareeZachary E. MartinotNathan MathisonEunice MatsetelaAndrei MesingerAbraham R. NebenBojan NikolicChuneeta D. NunhokeeNipanjana PatraSamantha PieterseNima Razavi-GhodsJon RinguetteJames RobnettKathryn RosieRaddwine SellCraig SmithAngelo SyceMax TegmarkNithyanandan ThyagarajanPeter K. G. WilliamsHaoxuan Zheng
2020-04-13
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
| Richard ChengMohammad Javad KhojastehAaron D. AmesJoel W. Burdick
2020-04-11
Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian Process-based Exploration-Exploitation
Sergei V. KalininMani ValletiRama K. VasudevanMaxim Ziatdinov
2020-04-09
Online Constrained Model-based Reinforcement Learning
Benjamin van NiekerkAndreas DamianouBenjamin Rosman
2020-04-07
Direct loss minimization algorithms for Bayesian predictors
Yadi WeiRishit ShethRoni Khardon
2020-04-07
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes
Moyan LiRaed Kontar
2020-04-06
Gaussian Process Boosting
| Fabio Sigrist
2020-04-06
Scalable Gaussian Processes, with Guarantees: Kernel Approximations and Deep Feature Extraction
Constantinos DaskalakisPetros DellaportasAristeidis Panos
2020-04-03
Predicting the outputs of finite networks trained with noisy gradients
Gadi NavehOded Ben-DavidHaim SompolinskyZohar Ringel
2020-04-02
Projection Pursuit Gaussian Process Regression
Gecheng ChenRui Tuo
2020-04-01
A Blackbox Yield Estimation Workflow with Gaussian Process Regression Applied to the Design of Electromagnetic Devices
| Mona FuhrländerSebastian Schöps
2020-03-30
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
Leif Erik AnderssonBart DoekemeijerDaan van der HoekJan-Willem van WingerdenLars Imsland
2020-03-30
Closed-loop Parameter Identification of Linear Dynamical Systems through the Lens of Feedback Channel Coding Theory
Ali Reza PedramTakashi Tanaka
2020-03-27
On Infinite-Width Hypernetworks
Etai LittwinTomer GalantiLior WolfGreg Yang
2020-03-27
Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
Max VeitDavid M. WilkinsYang YangRobert A. DiStasio Jr.Michele Ceriotti
2020-03-27
Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains
| Michele PeruzziSudipto BanerjeeAndrew O. Finley
2020-03-25
Preferential Batch Bayesian Optimization
Eero SiivolaAkash Kumar DhakaMichael Riis AndersenJavier GonzalezPablo Garcia MorenoAki Vehtari
2020-03-25
Detecting Multiple DLAs per Spectrum in SDSS DR12 with Gaussian Processes
Ming-Feng HoSimeon BirdRoman Garnett
2020-03-24
Efficient Gaussian Process Bandits by Believing only Informative Actions
Amrit Singh BediDheeraj PeddireddyVaneet AggarwalAlec 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 LyuMike Ludkovski
2020-03-19
Gaze-Sensing LEDs for Head Mounted Displays
Kaan AkşitJan KautzDavid Luebke
2020-03-18
Gaussian process aided function comparison using noisy scattered data
Abhinav PrakashRui TuoYu Ding
2020-03-17
The Elliptical Processes: a New Family of Flexible Stochastic Processes
Maria BånkestadJens SjölundJalil TaghiaThomas Schön
2020-03-13
Data-driven surrogate modelling and benchmarking for process equipment
Gabriel F. N. GonçalvesAssen BatchvarovYuyi LiuYuxin LiuLachlan MasonIndranil PanOmar K. Matar
2020-03-13
Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time
Hideaki ImamuraNontawat CharoenphakdeeFutoshi FutamiIssei SatoJunya HondaMasashi Sugiyama
2020-03-10
Channel Attention with Embedding Gaussian Process: A Probabilistic Methodology
Jiyang XieDongliang ChangZhanyu MaGuoqiang ZhangJun Guo
2020-03-10
Composition of kernel and acquisition functions for High Dimensional Bayesian Optimization
Antonio CandelieriIlaria GiordaniRiccardo PeregoFrancesco Archetti
2020-03-09
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
Feng YinZhidi LinYue XuQinglei KongDeshi LiSergios TheodoridisShuguangCui
2020-03-08
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone RossiMarkus HeinonenEdwin V. BonillaZheyang ShenMaurizio Filippone
2020-03-06
Multi-Output Gaussian Processes for Multi-Population Longevity Modeling
Nhan HuynhMike Ludkovski
2020-03-05
Corruption-Tolerant Gaussian Process Bandit Optimization
Ilija BogunovicAndreas KrauseJonathan Scarlett
2020-03-04
Regression via Implicit Models and Optimal Transport Cost Minimization
| Saurav ManchandaKhoa DoanPranjul YadavS. Sathiya Keerthi
2020-03-03
Gaussian Process Policy Optimization
Ashish RaoBidipta SarkarTejas Narayanan
2020-03-02
Imbalance Learning for Variable Star Classification
| Zafiirah HosenieRobert LyonBenjamin StappersArrykrishna MootoovalooVanessa McBride
2020-02-27
Towards new cross-validation-based estimators for Gaussian process regression: efficient adjoint computation of gradients
Sébastien PetitJulien BectSébastien da VeigaPaul FeliotEmmanuel Vazquez
2020-02-26
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
Alberto Dalla LiberaDiego RomeresDevesh K. JhaBill YerazunisDaniel Nikovski
2020-02-25
Generalized Bayesian Filtering via Sequential Monte Carlo
Ayman BoustatiÖmer Deniz AkyildizTheodoros DamoulasAdam Johansen
2020-02-23
Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast
Fatemeh NajibiDimitra ApostolopoulouEduardo Alonso
2020-02-23
Nonmyopic Gaussian Process Optimization with Macro-Actions
Dmitrii KharkovskiiChun Kai LingKian Hsiang Low
2020-02-22
Efficiently Sampling Functions from Gaussian Process Posteriors
| James T. WilsonViacheslav BorovitskiyAlexander TereninPeter MostowskyMarc Peter Deisenroth
2020-02-21
Development of modeling and control strategies for an approximated Gaussian process
Shisheng CuiChia-Jung Chang
2020-02-12
Regret Bounds for Noise-Free Bayesian Optimization
Sattar VakiliVictor PichenyNicolas Durrande
2020-02-12
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data
Johannes A. StorkTodor Stoyanov
2020-02-12
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation
Subhayan DeJolene BrittonMatthew ReynoldsRyan SkinnerKenneth JansenAlireza Doostan
2020-02-11
Graph Convolutional Gaussian Processes For Link Prediction
Felix L. OpolkaPietro Liò
2020-02-11
Gaussian process imputation of multiple financial series
Taco de WolffAlejandro CuevasFelipe Tobar
2020-02-11
Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems
Xiaoran RuanKe LiBilel DerbelArnaud Liefooghe
2020-02-08
Deep Moment Matching Kernel for Multi-source Gaussian Processes
Chi-Ken LuPatrick Shafto
2020-02-07
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Yun YuanXianfeng Terry YangZhao ZhangShandian Zhe
2020-02-06
One-Shot Bayes Opt with Probabilistic Population Based Training
Jack Parker-HolderVu NguyenStephen Roberts
2020-02-06
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction
Andrew PattersonAditya GahlawatNaira Hovakimyan
2020-02-05
Data-driven high-fidelity prediction of the equivalent sand-grain height of rough surfaces
Mostafa Aghaei JouybariJunlin YuanGiles J. Brereton
2020-02-04
Uncertainty Quantification for Bayesian Optimization
Rui TuoWenjia Wang
2020-02-04
Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation
Madhusanka Manimel WaduSumudu SamarakoonMehdi 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 PaolilloTeguh Santoso LembonoSylvain Calinon
2020-01-31
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Toni KarvonenGeorge WynneFilip TronarpChris J. OatesSimo Särkkä
2020-01-29
Convergence Guarantees for Gaussian Process Means with Misspecified Likelihoods and Smoothness
George WynneFrançois-Xavier BriolMark Girolami
2020-01-29
TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model
Yusuke UchiyamaKei Nakagawa
2020-01-29
Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption
Peter FennerEdward O. Pyzer-Knapp
2020-01-28
Bayesian nonparametric shared multi-sequence time series segmentation
Olga MikheevaIeva KazlauskaiteHedvig KjellströmCarl Henrik Ek
2020-01-27
Bayesian optimization for backpropagation in Monte-Carlo tree search
Yueqin LiNengli Lim
2020-01-25
The role of surrogate models in the development of digital twins of dynamic systems
Souvik ChakrabortySondipon AdhikariRanjan Ganguli
2020-01-25
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
| Daniele GammelliInon PeledFilipe RodriguesDario PacinoHaci A. KurtaranFrancisco C. Pereira
2020-01-21
Projection based Active Gaussian Process Regression for Pareto Front Modeling
Zhengqi GaoJun TaoYangfeng SuDian ZhouXuan Zeng
2020-01-20
Scalable Hyperparameter Optimization with Lazy Gaussian Processes
| Raju RamSabine MüllerFranz-Josef PfreundtNicolas R. GaugerJanis Keuper
2020-01-16
Doubly Sparse Variational Gaussian Processes
Vincent AdamStefanos EleftheriadisNicolas DurrandeArtem ArtemevJames Hensman
2020-01-15
Robust Gaussian Process Regression with a Bias Model
Chiwoo ParkDavid J. BorthNicholas S. WilsonChad N. HunterFritz J. Friedersdorf
2020-01-14
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
Siming BayerUte SpiskeJie LuoTobias GeimerWilliam M. Wells IIIMartin OstermeierRebecca FahrigArya NabaviChristoph BertIlker EyupogloAndreas Maier
2020-01-12
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
Yohan JungJinkyoo Park
2020-01-07
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Devanshu AgrawalTheodore PapamarkouJacob Hinkle
2020-01-03
Learning Human Postural Control with Hierarchical Acquisition Functions
Nils RottmannTjasa KunavarJan BabicJan PetersElmar Rueckert
2020-01-01
Learning Neural Surrogate Model for Warm-Starting Bayesian Optimization
Haotian ZhangJian SunZongben Xu
2020-01-01
Approximate Inference for Fully Bayesian Gaussian Process Regression
Vidhi LalchandCarl Edward Rasmussen
2019-12-31
Disentangling Trainability and Generalization in Deep Neural Networks
Lechao XiaoJeffrey PenningtonSamuel S. Schoenholz
2019-12-30
A statistical test for correspondence of texts to the Zipf-Mandelbrot law
Anik ChakrabartyMikhail ChebuninArtyom KovalevskiiIlya PupyshevNatalia ZakrevskayaQianqian 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 HajipourArash Mirabdolah LavasaniMohammad Eftekhari YazdiAmir MosaviShahaboddin ShamshirbandKwok-Wing Chau
2019-12-25
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Lukas HewingElena ArcariLukas P. FröhlichMelanie N. Zeilinger
2019-12-23
Tensor Basis Gaussian Process Models of Hyperelastic Materials
Ari FrankelReese JonesLaura Swiler
2019-12-23
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui ZhangChristian J. WalderEdwin V. BonillaMarian-Andrei RizoiuLexing Xie
2019-12-21
Methods for comparing uncertainty quantifications for material property predictions
Kevin TranWillie NeiswangerJunwoong YoonEric XingZachary W. Ulissi
2019-12-20
SSSpaNG! Stellar Spectra as Sparse, data-driven, Non-Gaussian processes
Stephen M. FeeneyBenjamin D. WandeltMelissa K. Ness
2019-12-19
Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems
| Wei HuangRichard Yi Da Xu
2019-12-19
Heteroscedastic Gaussian Process Regression on the Alkenone over Sea Surface Temperatures
| Taehee LeeCharles E. Lawrence
2019-12-18
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
Piyush PanditaNimish AwalgaonkarIlias BilionisJitesh Panchal
2019-12-16
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
Daniel Heestermans SvendsenLuca MartinoGustau Camps-Valls
2019-12-13
On the relationship between multitask neural networks and multitask Gaussian Processes
Karthikeyan KShubham Kumar BhartiPiyush Rai
2019-12-12
Learning and Optimization with Bayesian Hybrid Models
Elvis A. EugeneXian GaoAlexander W. Dowling
2019-12-12
Tensor Completion via Gaussian Process Based Initialization
Yermek KapushevIvan OseledetsEvgeny 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 MeehanKamalika Chaudhuri
2019-12-09
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies
| Juho TimonenHenrik MannerströmAki VehtariHarri Lähdesmäki
2019-12-07
Ordinal Bayesian Optimisation
Victor PichenySattar VakiliArtem Artemev
2019-12-05
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
Tong TengJie ChenYehong ZhangKian Hsiang Low
2019-12-05
Numerical Gaussian process Kalman filtering
Armin KüperSteffen Waldherr
2019-12-03
Nonnegative Gaussian process tomography for generalized segmented planar detectors
D. BlythN. MullinsE. GalyaevJ. 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 AjmeraShreya RajagopalRazi RehmanDevarajan Sridharan
2019-12-01
Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes
| Lingge LiDustin PlutaBabak ShahbabaNorbert FortinHernando OmbaoPierre 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 ZhangWenlong LyuFan YangChanghao YanDian ZhouXuan Zeng
2019-12-01
Richer priors for infinitely wide multi-layer perceptrons
Russell TsuchidaFred RoostaMarcus Gallagher
2019-11-29
Ultra-Reliable and Low-Latency Vehicular Communication: An Active Learning Approach
Mohamed K. Abdel-AzizSumudu SamarakoonMehdi BennisWalid Saad
2019-11-27
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process Modelling
Maxim ZiatdinovDohyung KimSabine NeumayerRama K. VasudevanLiam CollinsStephen JesseMahshid AhmadiSergei V. Kalinin
2019-11-26
Actively Learning Gaussian Process Dynamics
Mona Buisson-FenetFriedrich SolowjowSebastian Trimpe
2019-11-22
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model
| Juan-José GiraldoMauricio A. Álvarez
2019-11-22
Towards a complete 3D morphable model of the human head
| Stylianos PloumpisEvangelos VerverasEimear O' SullivanStylianos MoschoglouHaoyang WangNick PearsWilliam A. P. SmithBaris GecerStefanos Zafeiriou
2019-11-18
Iterative Construction of Gaussian Process Surrogate Models for Bayesian Inference
Leen AlawiehJonathan GoodmanJohn B. Bell
2019-11-17
Causal inference using Bayesian non-parametric quasi-experimental design
| Max HinneMarcel A. J. van GervenLuca Ambrogioni
2019-11-15
Conjugate Gradients for Kernel Machines
Simon BartelsPhilipp Hennig
2019-11-14
Uncertainty on Asynchronous Time Event Prediction
| Marin BilošBertrand CharpentierStephan Günnemann
2019-11-13
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Zhu LiAdrian Perez-SuayGustau Camps-VallsDino Sejdinovic
2019-11-11
Bayesian Active Learning for Structured Output Design
Kota MatsuiShunya KusakawaKeisuke AndoKentaro KutsukakeToru UjiharaIchiro Takeuchi
2019-11-09
Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees
Michael MaiwormDaniel LimonRolf Findeisen
2019-11-08
Non-parametric Probabilistic Load Flow using Gaussian Process Learning
Parikshit PareekChuan WangHung D. Nguyen
2019-11-08
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
Pavel BerkovichEric PerimWessel Bruinsma
2019-11-05
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data
Akitoshi MasudaYoshihiko SusukiManel Martínez-RamónAndrea MammoliAtsushi Ishigame
2019-11-04
On Batch Bayesian Optimization
Sayak Ray ChowdhuryAditya 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. HanukaJ. DurisJ. ShtalenkovaD. KennedyA. EdelenD. RatnerX. Huang
2019-11-04
Enhanced Convolutional Neural Tangent Kernels
Zhiyuan LiRuosong WangDingli YuSimon S. DuWei HuRuslan SalakhutdinovSanjeev Arora
2019-11-03
Continual Multi-task Gaussian Processes
| Pablo Moreno-MuñozAntonio Artés-RodríguezMauricio A. Álvarez
2019-10-31
Safe Exploration for Interactive Machine Learning
Matteo TurchettaFelix BerkenkampAndreas Krause
2019-10-30
Function-Space Distributions over Kernels
| Gregory W. BentonWesley J. MaddoxJayson P. SalkeyJulio AlbinatiAndrew 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 YanZhizhong LiYuanjun XiongHuahan Yan
2019-10-27
Implicit Posterior Variational Inference for Deep Gaussian Processes
| Haibin YuYizhou ChenZhongxiang DaiKian Hsiang LowPatrick Jaillet
2019-10-26
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search
| Colin WhiteWillie NeiswangerYash Savani
2019-10-25
Sparse Orthogonal Variational Inference for Gaussian Processes
| Jiaxin ShiMichalis K. TitsiasAndriy Mnih
2019-10-23
DCT Maps: Compact Differentiable Lidar Maps Based on the Cosine Transform
Alexander SchaeferLukas LuftWolfram Burgard
2019-10-23
Generalised learning of time-series: Ornstein-Uhlenbeck processes
Mehmet SüzenAlper Yegenoglu
2019-10-21
Bayesian Optimization Allowing for Common Random Numbers
Michael PearceMatthias PoloczekJuergen 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 GriffithsMiguel Garcia-OrtegonAlexander A. AldrickAlpha A. Lee
2019-10-17
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics
Chirag ModiUros Seljak
2019-10-16
Parametric Gaussian Process Regressors
Martin JankowiakGeoff PleissJacob R. Gardner
2019-10-16
The Renyi Gaussian Process: Towards Improved Generalization
Xubo YueRaed Kontar
2019-10-15
Regularized Sparse Gaussian Processes
Rui MengHerbert LeeSoper BradenPriyadip Ray
2019-10-13
Bayesian Optimization using Pseudo-Points
Chao QianHang XiongKe Xue
2019-10-12
Evolving Gaussian Process kernels from elementary mathematical expressions
Ibai RomanRoberto SantanaAlexander MendiburuJose A. Lozano
2019-10-11
Learning from demonstration with model-based Gaussian process
Noémie JaquierDavid GinsbourgerSylvain Calinon
2019-10-11
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Julius von KügelgenPaul K RubensteinBernhard SchölkopfAdrian Weller
2019-10-09
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong MaRenke HuangDavid Barajas-SolanoRamakrishna TipireddyAlexandre M. Tartakovsky
2019-10-09
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
Weiyang ZhangWenshuo WangDing Zhao
2019-10-08
mfEGRA: Multifidelity Efficient Global Reliability Analysis
Anirban ChaudhuriAlexandre N. MarquesKaren E. Willcox
2019-10-06
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Zenan LiXiaoxing MaChang XuJingwei XuChun CaoJian Lü
2019-10-06
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning
Sahand Rezaei-ShoshtariDavid MegerInna Sharf
2019-10-05
Dynamic Embedding on Textual Networks via a Gaussian Process
| Pengyu ChengYitong LiXinyuan ZhangLiqun ChengDavid CarlsonLawrence Carin
2019-10-05
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
Yichi ZhangDaniel ApleyWei Chen
2019-10-03
Kepler data analysis: non-Gaussian noise and Fourier Gaussian process analysis of star variability
Jakob RobnikUroš Seljak
2019-10-02
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
Margherita RosnatiVincent Fortuin
2019-09-27
Deep recurrent Gaussian process with variational Sparse Spectrum approximation
Roman FöllBernard HaasdonkMarkus HanselmannHolger Ulmer
2019-09-27
Debiased Bayesian inference for average treatment effects
| Kolyan RayBotond Szabo
2019-09-26
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario
Thanh LeVasant Honavar
2019-09-25
Adversarial Vulnerability Bounds for Gaussian Process Classification
Michael Thomas SmithKathrin GrosseMichael BackesMauricio A Alvarez
2019-09-19
Bayesian Optimization under Heavy-tailed Payoffs
| Sayak Ray ChowdhuryAditya Gopalan
2019-09-16
MCTS-based Automated Negotiation Agent
Cédric BuronZahia GuessoumSylvain Ductor
2019-09-12
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
Arpan KusariJonathan 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 ChenGöran WestlingBenoni B. EdinPatrick van der Smagt
2019-09-09
Unsupervised Image Regression for Heterogeneous Change Detection
Luigi T. LuppinoFilippo M. BianchiGabriele MoserStian N. Anfinsen
2019-09-07
Deep kernel learning for integral measurements
Carl JidlingJohannes HendriksThomas B. SchönAdrian Wills
2019-09-04
Latent Gaussian process with composite likelihoods for data-driven disease stratification
Siddharth RamchandranMiika KoskinenHarri Lähdesmäki
2019-09-04
Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
Lixue ChengNikola B. KovachkiMatthew WelbornThomas 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 WangMarco 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 AkbariRumi Chunara
2019-08-24
Adaptive Configuration Oracle for Online Portfolio Selection Methods
Favour M. NyikosaMichael A. OsborneStephen J. Roberts
2019-08-22
Are Registration Uncertainty and Error Monotonically Associated
Jie LuoSarah FriskenDuo WangAlexandra GolbyMasashi SugiyamaWilliam M. Wells III
2019-08-21
Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy
Collin A. PolitschJessi Cisewski-KeheRupert A. C. CroftLarry Wasserman
2019-08-20
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models
Guoli SongShuhui WangQingming HuangQi Tian
2019-08-14
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
Ye XueDiego KlabjanYuan Luo
2019-08-12
Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
YoungJoon YooSangdoo YunHyung Jin ChangYiannis DemirisJin Young Choi
2019-08-12
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
| Ksenia KorovinaSailun XuKirthevasan KandasamyWillie NeiswangerBarnabas PoczosJeff SchneiderEric 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 ShabaniSaeed SamadianfardMohammad Taghi SattariShahab ShamshirbandAmir MosaviTibor KmetAnnamaria R. Varkonyi-Koczy
2019-08-01
Scalable Bayesian Non-linear Matrix Completion
Xiangju QinPaul BlomstedtSamuel Kaski
2019-07-31
Sequential Learning of Active Subspaces
Nathan WycoffMickael BinoisStefan M. Wild
2019-07-26
Bayesian Volumetric Autoregressive generative models for better semisupervised learning
| Guilherme PomboRobert GrayTom VarsavskyJohn AshburnerParashkev Nachev
2019-07-26
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Sayan GhoshJesper KristensenYiming ZhangWaad SubberLiping Wang
2019-07-26
Towards Scalable Gaussian Process Modeling
Piyush PanditaJesper KristensenLiping Wang
2019-07-25
Accelerating Experimental Design by Incorporating Experimenter Hunches
Cheng LiSantu RanaSunil GuptaVu NguyenSvetha VenkateshAlessandra SuttiDavid RubinTeo SlezakMurray HeightMazher MohammedIan Gibson
2019-07-22
A Multiple Continuous Signal Alignment Algorithm with Gaussian Process Profiles and an Application to Paleoceanography
| Taehee LeeLorraine E. LisieckiDevin RandGeoffrey GebbieCharles E. Lawrence
2019-07-20
Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs
Yusuke TanakaToshiyuki TanakaTomoharu IwataTakeshi KurashimaMaya OkawaYasunori AkagiHiroyuki Toda
2019-07-19
Kernel Mode Decomposition and programmable/interpretable regression networks
| Houman OwhadiClint ScovelGene Ryan Yoo
2019-07-19
Latent Function Decomposition for Forecasting Li-ion Battery Cells Capacity: A Multi-Output Convolved Gaussian Process Approach
Abdallah A. ChehadeAla A. Hussein
2019-07-19
Structured Variational Inference in Unstable Gaussian Process State Space Models
| Silvan MelchiorSebastian CuriFelix BerkenkampAndreas Krause
2019-07-16
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach
Bin LiuYu QiKe-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 BatschAlireza DaneshkhahMadeline CheahStratis KanarachosAnthony Baxendale
2019-07-11
Gaussian Processes for Analyzing Positioned Trajectories in Sports
Yuxin ZhaoFeng YinFredrik GunnarssonFredrik Hultkrantz
2019-07-05
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
Akshay IyerYichi ZhangAditya PrasadSiyu TaoYixing WangLinda SchadlerL Catherine BrinsonWei Chen
2019-07-04
Unscented Gaussian Process Latent Variable Model: learning from uncertain inputs with intractable kernels
Daniel Augusto R. M. A. de SouzaCésar Lincoln C. MattosJoão Paulo P. Gomes
2019-07-03
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
Yuqing ZhangNeil Walton
2019-07-02
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
| Christoph HeindlThomas PönitzGernot StüblAndreas PichlerJosef Scharinger
2019-07-01
Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning
Edwin SimpsonErik-L{\^a}n Do DinhTristan MillerIryna Gurevych
2019-07-01
Multi-objective multi-generation Gaussian process optimizer for design optimization
Xiaobiao HuangMinghao SongZhe Zhang
2019-06-29
Learning Fair Representations for Kernel Models
| Zilong TanSamuel YeomMatt FredriksonAmeet Talwalkar
2019-06-27
Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics
| Sebastian RiedelFreek Stulp
2019-06-27
Modulating Surrogates for Bayesian Optimization
Erik BodinMarkus KaiserIeva KazlauskaiteZhenwen DaiNeill D. F. CampbellCarl Henrik Ek
2019-06-26
Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
| Yaohui GuoVinay Varma KalidindiMansur AriefWenshuo WangJiacheng ZhuHuei PengDing Zhao
2019-06-25
Compositionally-Warped Gaussian Processes
Gonzalo RiosFelipe Tobar
2019-06-23
Sparse Spectrum Gaussian Process for Bayesian Optimization
Ang YangCheng LiSantu RanaSunil GuptaSvetha Venkatesh
2019-06-21
Tomographic Reconstruction of Triaxial Strain Fields from Bragg-Edge Neutron Imaging
| J. N. HendriksA. W. T. GreggR. R. JacksonC. M. WensrichA. WillsA. S. TremsinT. ShinoharaV. LuzinO. Kirstein
2019-06-20
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
| Binxin RuAhsan S. AlviVu NguyenMichael A. OsborneStephen J Roberts
2019-06-20
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
| Csaba TothHarald Oberhauser
2019-06-19
Bayesian inverse regression for dimension reduction with small datasets
Xin CaiGuang LinJinglai Li
2019-06-19
Multi-resolution Multi-task Gaussian Processes
| Oliver HamelijnckTheodoros DamoulasKangrui WangMark Girolami
2019-06-19
Bayesian Optimization with Binary Auxiliary Information
Yehong ZhangZhongxiang DaiKian Hsiang Low
2019-06-17
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
| Alessandro Davide IalongoMark van der WilkJames HensmanCarl Edward Rasmussen
2019-06-13
Robust Regression for Safe Exploration in Control
Anqi LiuGuanya ShiSoon-Jo ChungAnima AnandkumarYisong Yue
2019-06-13
Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective
Omry CohenOr MalkaZohar Ringel
2019-06-12
Towards Inverse Reinforcement Learning for Limit Order Book Dynamics
Jacobo Roa-VicensCyrine ChtourouAngelos FilosFrancisco RullanYarin GalRicardo Silva
2019-06-11
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories
Guangyi ZhangReza AshrafiAnne JuutiKirsi PietiläinenPekka 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 ZhaiHung D. Nguyen
2019-06-09
A Variant of Gaussian Process Dynamical Systems
Jing ZhaoJingjing FeiShiliang Sun
2019-06-09
Structured Variational Inference in Continuous Cox Process Models
| Virginia AgliettiEdwin V. BonillaTheodoros DamoulasSally 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 XuFeng YinJiawei ZhangZhi-Quan LuoShuguang Cui
2019-06-06
Bayesian Optimization of Composite Functions
| Raul AstudilloPeter I. Frazier
2019-06-04
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Armin LedererJonas UmlauftSandra Hirche
2019-06-04
Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement
Ming LinXiaomin SongQi QianHao LiLiang SunShenghuo ZhuRong Jin
2019-06-03
Resolving Overlapping Convex Objects in Silhouette Images by Concavity Analysis and Gaussian Process
Sahar ZafariMariia MurashkinaTuomas EerolaJouni SampoHeikki KälviäinenHeikki Haario
2019-06-03
Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel
| Xin QiuElliot MeyersonRisto Miikkulainen
2019-06-03
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Li-Fang ChengBianca DumitrascuMichael ZhangCorey ChiversMichael DraugelisKai LiBarbara E. Engelhardt
2019-06-01
Neural Likelihoods for Multi-Output Gaussian Processes
Martin JankowiakJacob Gardner
2019-05-31
Enriched Mixtures of Gaussian Process Experts
Charles W. L. GaddSara WadeAlexis Boukouvalas
2019-05-30
Efficient EM-Variational Inference for Hawkes Process
Feng ZhouZhidong LiXuhui FanYang WangArcot SowmyaFang Chen
2019-05-29
Adversarial Robustness Guarantees for Classification with Gaussian Processes
Arno BlaasAndrea PataneLuca LaurentiLuca CardelliMarta KwiatkowskaStephen Roberts
2019-05-28
Recursive Estimation for Sparse Gaussian Process Regression
| Manuel SchürchDario AzzimontiAlessio BenavoliMarco Zaffalon
2019-05-28
Scalable Training of Inference Networks for Gaussian-Process Models
| Jiaxin ShiMohammad Emtiyaz KhanJun Zhu
2019-05-27
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
| Théo Galy-FajouFlorian WenzelChristian DonnerManfred Opper
2019-05-23
Learning spectrograms with convolutional spectral kernels
Zheyang ShenMarkus HeinonenSamuel Kaski
2019-05-23
A Bulirsch-Stoer algorithm using Gaussian processes
Philip G. BreenChristopher 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 KimSeungjin Choi
2019-05-18
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations
Timur TakhtaganovZarija LukicJuliane MuellerDmitriy Morozov
2019-05-17
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
Raj AgrawalJonathan H. HugginsBrian TrippeTamara Broderick
2019-05-16
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
| Tim PearceRussell TsuchidaMohamed ZakiAlexandra BrintrupAndy Neely
2019-05-15
Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes
Chengyao SunWeisi Guo
2019-05-15
Online Anomaly Detection with Sparse Gaussian Processes
Jingjing FeiShiliang 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 HuangTucker Hermans
2019-05-10
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
| Francisco Sahli CostabalParis PerdikarisEllen KuhlDaniel E. Hurtado
2019-05-09
Knowing The What But Not The Where in Bayesian Optimization
Vu NguyenMichael A. Osborne
2019-05-07
A deep learning approach for analyzing the composition of chemometric data
Muhammad BilalMohib Ullah
2019-05-07
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
| Marko JärvenpääMichael GutmannAki VehtariPekka Marttinen
2019-05-03
Modular Deep Probabilistic Programming
| Zhenwen DaiEric MeissnerNeil D. Lawrence
2019-05-01
A data-efficient geometrically inspired polynomial kernel for robot inverse dynamics
Alberto Dalla LiberaRuggero Carli
2019-04-30
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
Kaifeng YangMichael EmmerichAndré DeutzThomas Bäck
2019-04-26
A Bayesian Approach for the Robust Optimisation of Expensive-To-Evaluate Functions
Nicholas D. SandersRichard M. EversonJonathan E. FieldsendAlma 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 RudovicYuria UtsumiRicardo GuerreroKelly PetersonDaniel RueckertRosalind W. Picard
2019-04-19
A Bayesian Perspective on the Deep Image Prior
| Zezhou ChengMatheus GadelhaSubhransu MajiDaniel Sheldon
2019-04-16
Multi-View Stereo by Temporal Nonparametric Fusion
| Yuxin HouJuho KannalaArno 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. ManiruzzamanMd. Jahanur RahmanBenojir AhammedMd. Menhazul AbedinHarman S. SuriMainak BiswasAyman El-BazPetros BangeasGeorgios TsoulfasJasjit S. Suri
2019-04-08
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Edwin SimpsonSteven ReeceStephen J. Roberts
2019-04-05
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
Andrew PattersonArun LakshmananNaira Hovakimyan
2019-04-04
On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events
Jonathan VandermauseSteven B. TorrisiSimon BatznerAlexie M. KolpakBoris Kozinsky
2019-04-03
Sentiment analysis with genetically evolved Gaussian kernels
Ibai RomanAlexander MendiburuRoberto SantanaJose A. Lozano
2019-04-01
MCTS-based Automated Negotiation Agent (Extended Abstract)
Cédric BuronZahia GuessoumSylvain Ductor
2019-03-29
A Gaussian process latent force model for joint input-state estimation in linear structural systems
Rajdip NayekSouvik ChakrabortySriram Narasimhan
2019-03-29
Using Gaussian process regression for efficient parameter reconstruction
Philipp-Immanuel SchneiderMartin HammerschmidtLin ZschiedrichSven Burger
2019-03-28
Pixelation is NOT Done in Videos Yet
Jizhe ZhouChi-Man PunYingYu Wang
2019-03-26
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints
Nimish AwalgaonkarIlias BilionisXiaoqi LiuPanagiota KaravaAthanasios Tzempelikos
2019-03-21
Deep Gaussian Processes for Multi-fidelity Modeling
Kurt CutajarMark PullinAndreas DamianouNeil LawrenceJavier González
2019-03-18
Financial Applications of Gaussian Processes and Bayesian Optimization
Joan GonzalvezEdmond LezmiThierry RoncalliJiali Xu
2019-03-12
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations
Astrid DahlEdwin V. Bonilla
2019-03-10
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Seokhyun ChungRaed Kontar
2019-03-09
Rates of Convergence for Sparse Variational Gaussian Process Regression
| David R. BurtCarl E. RasmussenMark van der Wilk
2019-03-08
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation
Jiameng FanWenchao Li
2019-03-06
A Bayesian Approach to Triaxial Strain Tomography from High-energy X-ray Diffraction
J. N. HendriksC. M. WensrichA. Wills
2019-03-06
Deep Learning and Gaussian Process based Band Assignment in Dual Band Systems
Daoud BurghalRui WangAndreas F. Molisch
2019-02-28
Estimating Local Function Complexity via Mixture of Gaussian Processes
Danny PankninShinichi NakajimaThanh Binh BuiKlaus-Robert Müller
2019-02-27
Active learning via informed search in movement parameter space for efficient robot task learning and transfer
| Nemanja RakicevicPetar Kormushev
2019-02-21
Stable Bayesian Optimisation via Direct Stability Quantification
Alistair ShiltonSunil GuptaSantu RanaSvetha VenkateshMajid AbdolshahDang Nguyen
2019-02-21
Bayesian optimisation under uncertain inputs
Rafael OliveiraLionel OttFabio Ramos
2019-02-21
Gaussian Process Priors for Dynamic Paired Comparison Modelling
| Martin Ingram
2019-02-20
Multifidelity Bayesian Optimization for Binomial Output
Leonid MatyushinAlexey ZaytsevOleg AlenkinAndrey Ustuzhanin
2019-02-19
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon LeeLechao XiaoSamuel S. SchoenholzYasaman BahriRoman NovakJascha Sohl-DicksteinJeffrey Pennington
2019-02-18
The Kalai-Smorodinski solution for many-objective Bayesian optimization
Mickaël BinoisVictor PichenyPatrick TaillandierAbderrahmane Habbal
2019-02-18
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Yue XuFeng YinWenjun XuJiaru LinShuguang 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 DunckerGergo BohnerJulien BoussardManeesh Sahani
2019-02-12
Multi-objective Bayesian optimisation with preferences over objectives
Majid AbdolshahAlistair ShiltonSantu RanaSunil GuptaSvetha 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 MoorMax HornBastian RieckDamian RoqueiroKarsten Borgwardt
2019-02-05
Combinatorial Bayesian Optimization using the Graph Cartesian Product
| Changyong OhJakub M. TomczakEfstratios GavvesMax Welling
2019-02-01
Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach
Raed KontarGarvesh RaskuttiShiyu Zhou
2019-01-31
End-to-End Probabilistic Inference for Nonstationary Audio Analysis
| William J. WilkinsonMichael Riis AndersenJoshua D. ReissDan StowellArno Solin
2019-01-31
Geometric fluid approximation for general continuous-time Markov chains
Michalis MichaelidesJane HillstonGuido Sanguinetti
2019-01-31
Functional Regularisation for Continual Learning with Gaussian Processes
Michalis K. TitsiasJonathan SchwarzAlexander G. de G. MatthewsRazvan PascanuYee Whye Teh
2019-01-31
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis
Kohei HayashiMasaaki ImaizumiYuichi Yoshida
2019-01-28
Meta-Learning Mean Functions for Gaussian Processes
Vincent FortuinHeiko StrathmannGunnar Rätsch
2019-01-23
Active Learning with Gaussian Processes for High Throughput Phenotyping
| Sumit KumarWenhao LuoGeorge KantorKatia Sycara
2019-01-21
Modeling and inference of spatio-temporal protein dynamics across brain networks
Sara GarbarinoMarco 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 BerkenkampAngela P. SchoelligAndreas Krause
2019-01-10
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Randy ArdywibowoGuang ZhaoZhangyang WangBobak MortazaviShuai HuangXiaoning Qian
2019-01-08
Variational bridge constructs for approximate Gaussian process regression
Wil O C WardMauricio A Álvarez
2019-01-07
Forecasting residential gas demand: machine learning approaches and seasonal role of temperature forecasts
Andrea MarzialiEmanuele FabbianiGiuseppe De Nicolao
2019-01-04
Approximate Inference for Multiplicative Latent Force Models
Daniel J. TaitBruce J. Worton
2018-12-31
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
| Jamie FairbrotherChristopher NemethMaxime RischardJohanni BreaThomas Pinder
2018-12-21
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
| Xiaoliang DaiPeizhao ZhangBichen WuHongxu YinFei SunYanghan WangMarat DukhanYunqing HuYiming WuYangqing JiaPeter VajdaMatt UyttendaeleNiraj K. Jha
2018-12-21
Towards an Evolvable Cancer Treatment Simulator
Richard J. PreenLarry BullAndrew Adamatzky
2018-12-19
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion
Seungjoon LeeFelix DietrichGeorge E. KarniadakisIoannis G. Kevrekidis
2018-12-16
Efficient Model-Free Reinforcement Learning Using Gaussian Process
| Ying FanLetian ChenYizhou Wang
2018-12-11
Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest
Keir K. RogersHiranya V. PeirisAndrew PontzenSimeon BirdLicia VerdeAndreu Font-Ribera
2018-12-11
The Limitations of Model Uncertainty in Adversarial Settings
Kathrin GrosseDavid PfaffMichael Thomas SmithMichael Backes
2018-12-06
Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph ZimmerMona MeisterDuy Nguyen-Tuong
2018-12-01
Learning a latent manifold of odor representations from neural responses in piriform cortex
Anqi WuStan PashkovskiSandeep R. DattaJonathan W. Pillow
2018-12-01
A Bayes-Sard Cubature Method
Toni KarvonenChris J. OatesSimo Sarkka
2018-12-01
Scalable Hyperparameter Transfer Learning
Valerio PerroneRodolphe JenattonMatthias W. SeegerCedric Archambeau
2018-12-01
Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments
Mahdi ImaniSeyede Fatemeh GhoreishiUlisses M. Braga-Neto
2018-12-01
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Rico AngellDaniel R. Sheldon
2018-12-01
Temporal alignment and latent Gaussian process factor inference in population spike trains
Lea DunckerManeesh Sahani
2018-12-01
Deep Factors with Gaussian Processes for Forecasting
Danielle C. MaddixYuyang WangAlex Smola
2018-11-30
Neural Non-Stationary Spectral Kernel
| Sami RemesMarkus HeinonenSamuel Kaski
2018-11-27
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Thang D. BuiCuong V. NguyenSiddharth SwaroopRichard E. Turner
2018-11-27
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Xiu YangDavid Barajas-SolanoGuzel TartakovskyAlexandre Tartakovsky
2018-11-24
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
| Zi WangBeomjoon KimLeslie Pack Kaelbling
2018-11-23
Mixed Likelihood Gaussian Process Latent Variable Model
Samuel MurrayHedvig Kjellström
2018-11-19
Recursive Sparse Pseudo-input Gaussian Process SARSA
John MartinBrendan Englot
2018-11-17
Mean Square Prediction Error of Misspecified Gaussian Process Models
Thomas BeckersJonas UmlauftSandra Hirche
2018-11-16
Reachability-based safe learning for optimal control problem
Stanislav FedorovAntonio Candelieri
2018-11-09
Unifying Probabilistic Models for Time-Frequency Analysis
| William J. WilkinsonMichael Riis AndersenJoshua D. ReissDan StowellArno Solin
2018-11-06
Large-scale Heteroscedastic Regression via Gaussian Process
Haitao LiuYew-Soon OngJianfei Cai
2018-11-03
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
Haitao LiuJianfei CaiYew-Soon OngYi Wang
2018-11-03
Multiplicative Latent Force Models
Daniel J. TaitBruce J. Worton
2018-11-01
An Efficient Implementation of Riemannian Manifold Hamiltonian Monte Carlo for Gaussian Process Models
Ulrich PaquetMarco Fraccaro
2018-10-28
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
William HerlandsDaniel B. NeillHannes NickischAndrew Gordon Wilson
2018-10-28
Gaussian Process Prior Variational Autoencoders
| Francesco Paolo CasaleAdrian V DalcaLuca SagliettiJennifer ListgartenNicolo Fusi
2018-10-28
Adversarially Robust Optimization with Gaussian Processes
Ilija BogunovicJonathan ScarlettStefanie JegelkaVolkan Cevher
2018-10-25
Data Association with Gaussian Processes
Markus KaiserClemens OtteThomas RunklerCarl Henrik Ek
2018-10-16
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar MärtensKieran R. CampbellChristopher Yau
2018-10-16
Hyperparameter Learning via Distributional Transfer
| Ho Chung Leon LawPeilin ZhaoLucian ChanJunzhou HuangDino Sejdinovic
2018-10-15
Non-linear process convolutions for multi-output Gaussian processes
Mauricio A. ÁlvarezWil O. C. WardCristian Guarnizo
2018-10-10
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
Filip TronarpHans KerstingSimo SärkkäPhilipp Hennig
2018-10-08
Deep convolutional Gaussian processes
| Kenneth BlomqvistSamuel KaskiMarkus Heinonen
2018-10-06
GPdoemd: a Python package for design of experiments for model discrimination
| Simon OlofssonLukas HebingSebastian NiedenführMarc Peter DeisenrothRuth Misener
2018-10-05
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho LeeYoonho LeeJungtaek KimAdam R. KosiorekSeungjin ChoiYee Whye Teh
2018-10-01
Adaptive Gaussian process surrogates for Bayesian inference
Timur TakhtaganovJuliane Müller
2018-09-27
High-accuracy mass, spin, and recoil predictions of generic black-hole merger remnants
Vijay VarmaDavide GerosaFrançois HébertLeo C. SteinHao Zhang
2018-09-24
Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models
| Shoubo HuZhitang ChenVahid Partovi NiaLaiwan ChanYanhui Geng
2018-09-23
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
Yusuke TanakaTomoharu IwataToshiyuki TanakaTakeshi KurashimaMaya OkawaHiroyuki Toda
2018-09-21
InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics
| Young-Jin ParkHan-Lim Choi
2018-09-19
A Generalized Representer Theorem for Hilbert Space - Valued Functions
Sanket DiwaleColin Jones
2018-09-19
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze
Diego RomeresDevesh JhaAlberto Dalla LiberaWilliam YerazunisDaniel Nikovski
2018-09-13
Gaussian process classification using posterior linearisation
Ángel F. García-FernándezFilip TronarpSimo Särkkä
2018-09-13
Gaussian Process Classification for Variable Fidelity Data
| Nikita KlyuchnikovEvgeny Burnaev
2018-09-13
Probabilistic approach to limited-data computed tomography reconstruction
Zenith PurishaCarl JidlingNiklas WahlströmSimo SärkkäThomas B. Schön
2018-09-11
Physics-Informed Kriging: A Physics-Informed Gaussian Process Regression Method for Data-Model Convergence
Xiu YangGuzel TartakovskyAlexandre Tartakovsky
2018-09-10
Gaussian Process Regression for Binned Data
Michael Thomas SmithMauricio A AlvarezNeil 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 LangFranz M. J. PfisterJakob FröhnerKian AbedinpourDaniel PichlerUrban FietzekTerry T. UmDana KulićSatoshi EndoSandra 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 AaltoLauri ViitasaariPauliina IlmonenLaurent MombaertsJorge Goncalves
2018-08-24
Deep Convolutional Networks as shallow Gaussian Processes
| Adrià Garriga-AlonsoCarl Edward RasmussenLaurence Aitchison
2018-08-16
Locally-adaptive Bayesian nonparametric inference for phylodynamics
James R. FaulknerAndrew F. MageeBeth ShapiroVladimir N. Minin
2018-08-13
Exploiting Structure for Fast Kernel Learning
| Trefor W. EvansPrasanth B. Nair
2018-08-09
Improving Temporal Interpolation of Head and Body Pose using Gaussian Process Regression in a Matrix Completion Setting
Stephanie TanHayley Hung
2018-08-06
Machine Learning of Space-Fractional Differential Equations
Mamikon GulianMaziar RaissiParis PerdikarisGeorge Karniadakis
2018-08-02
Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes
Christian DonnerManfred Opper
2018-08-02
A Data-Efficient Approach to Precise and Controlled Pushing
Maria BauzaFrancois R. HoganAlberto Rodriguez
2018-07-26
Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization
Zhaozhong ChenChristoffer HeckmanSimon JulierNisar Ahmed
2018-07-23
EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning
Kunal MendaKatherine Driggs-CampbellMykel J. Kochenderfer
2018-07-22
Deep Learning for Epidemiological Predictions
WuYuexin YangYiming NishiuraHiroshi SaitohMasaya
2018-07-21
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
Xiong LyuMickael BinoisMichael Ludkovski
2018-07-18
Battery health prediction under generalized conditions using a Gaussian process transition model
Robert R. RichardsonMichael A. OsborneDavid A. Howey
2018-07-17
Machine Learning of Energetic Material Properties
Brian C. BarnesDaniel C. EltonZois BoukouvalasDeCarlos E. TaylorWilliam D. MattsonMark D. FugePeter W. Chung
2018-07-17
Spatio-Temporal Structured Sparse Regression with Hierarchical Gaussian Process Priors
Danil KuzinOlga IsupovaLyudmila Mihaylova
2018-07-15
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Andrew R. LawrenceCarl Henrik EkNeill D. F. Campbell
2018-07-12
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
| Steven AtkinsonNicholas Zabaras
2018-07-11
An Empirical Approach For Probing the Definiteness of Kernels
Martin ZaeffererThomas Bartz-BeielsteinGünter Rudolph
2018-07-10
Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning
Danil KuzinLe YangOlga IsupovaLyudmila 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