99 citations · 314 across the 26 of their papers we have counts for
26 papers
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
Youngsoo Baek, Samuel I. Berchuck, Sayan Mukherjee
In this paper we compare and contrast the behavior of the posterior predictive distribution to the risk of the maximum a posteriori estimator for the random features regression mod…
Bi-Objective Lexicographic Optimization in Markov Decision Processes with Related Objectives
Damien Busatto-Gaston, Debraj Chakraborty, Anirban Majumdar +3
We consider lexicographic bi-objective problems on Markov Decision Processes (MDPs), where we optimize one objective while guaranteeing optimality of another. We propose a two-stag…
Minimum -distance estimators for finite mixing measures
Yun Wei, Sayan Mukherjee, XuanLong Nguyen
Finite mixture models have long been used across a variety of fields in engineering and sciences. Recently there has been a great deal of interest in quantifying the convergence be…
Global Optimality of Elman-type RNN in the Mean-Field Regime
Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime. Specifically, we show convergence of gradient descent training dynamics of the R…
Why Neural Networks Work
Sayandev Mukherjee, Bernardo A. Huberman
We argue that many properties of fully-connected feedforward neural networks (FCNNs), also called multi-layer perceptrons (MLPs), are explainable from the analysis of a single pair…
Generalized Bayes Approach to Inverse Problems with Model Misspecification
Youngsoo Baek, Wilkins Aquino, Sayan Mukherjee
We propose a general framework for obtaining probabilistic solutions to PDE-based inverse problems. Bayesian methods are attractive for uncertainty quantification but assume knowle…