Search Results for author:
Found 27 papers, 9 papers with code
Scalable Bayesian inference for the generalized linear mixed model
The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings.
Global Optimality of Elman-type RNN in the Mean-Field Regime
We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime.
Concentration inequalities and optimal number of layers for stochastic deep neural networks
We state concentration inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN.
Tight query complexity bounds for learning graph partitions
Given a partition of a graph into connected components, the membership oracle asserts whether any two vertices of the graph lie in the same component or not.
Accelerating Markov Random Field Inference with Uncertainty Quantification
In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling.
A Methodology for Exploring Deep Convolutional Features in Relation to Hand-Crafted Features with an Application to Music Audio Modeling
In this work, we instead take the perspective of relating deep features to well-studied, hand-crafted features that are meaningful for the application of interest.
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing
Reservoir computing and deep sequential models, on the one hand, have demonstrated efficient, robust, and superior performance in modeling simple and chaotic dynamical systems.
Multiple Hypothesis Testing with Persistent Homology
Multiple hypothesis testing requires a control procedure.
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series
Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series.
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Applying the framework to guide design space exploration shows that statistical robustness comparable to floating-point software can be achieved by slightly increasing the bit representation, without floating-point hardware requirements.
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Although a common approach is to compare the end-point result quality using community-standard benchmarks and metrics, we claim a probabilistic architecture should provide some measure (or guarantee) of statistical robustness.
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well.
Adaptive particle-based approximations of the Gibbs posterior for inverse problems
To manage the computational cost of propagating increasing numbers of particles through the loss function, we employ a recently developed local reduced basis method to build an efficient surrogate loss function that is used in the Gibbs update formula in place of the true loss.
Subspace Clustering through Sub-Clusters
The problem of dimension reduction is of increasing importance in modern data analysis.
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models
We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation.
Learning Integral Representations of Gaussian Processes
We propose a representation of Gaussian processes (GPs) based on powers of the integral operator defined by a kernel function, we call these stochastic processes integral Gaussian processes (IGPs).
Partitioned Tensor Factorizations for Learning Mixed Membership Models
We present an efficient algorithm for learning mixed membership models when the number of variables p is much larger than the number of hidden components k. This algorithm reduces the computational complexity of state-of-the-art tensor methods, which require decomposing an $O(p^3)$ tensor, to factorizing $O(p/k)$ sub-tensors each of size $O(k^3)$.
Efficient Learning of Mixed Membership Models
We present an efficient algorithm for learning mixed membership models when the number of variables $p$ is much larger than the number of hidden components $k$.
Functional Data Analysis using a Topological Summary Statistic: the Smooth Euler Characteristic Transform
We introduce a novel statistic, the smooth Euler characteristic transform (SECT), which is designed to integrate shape information into regression models by representing shapes and surfaces as a collection of curves.
Applications
Fast moment estimation for generalized latent Dirichlet models
We illustrate the utility of our approach on simulated data, comparing results from MELD to alternative methods, and we show the promise of our approach through the application of MELD to several data sets.
Bayesian Approximate Kernel Regression with Variable Selection
State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively.
Adaptive Randomized Dimension Reduction on Massive Data
In this paper we develop an approach for dimension reduction that exploits the assumption of low rank structure in high dimensional data to gain both computational and statistical advantages.
Bayesian group latent factor analysis with structured sparsity
Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for an observation matrix with p features across n samples.
The Information Geometry of Mirror Descent
Using this equivalence, it follows that (1) mirror descent is the steepest descent direction along the Riemannian manifold of the exponential family; (2) mirror descent with log-likelihood loss applied to parameter estimation in exponential families asymptotically achieves the classical Cram\'er-Rao lower bound and (3) natural gradient descent for manifolds corresponding to exponential families can be implemented as a first-order method through mirror descent.
Randomized Dimension Reduction on Massive Data
Scalability of statistical estimators is of increasing importance in modern applications and dimension reduction is often used to extract relevant information from data.
Geometric Representations of Random Hypergraphs
A parametrization of hypergraphs based on the geometry of points in $\mathbf{R}^d$ is developed.
Localized Sliced Inverse Regression
We developed localized sliced inverse regression for supervised dimension reduction.
