68 citations · 353 across the 57 of their papers we have counts for
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Robust Bayesian Inference on Riemannian Submanifold
Rong Tang, Anirban Bhattacharya, Debdeep Pati +1
Manifold-valued parameters routinely arise in modern statistical applications such as in medical imaging, robotics, and computer vision, to name a few. While traditional Bayesian a…
Constrained Reweighting of Distributions: an Optimal Transport Approach
Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
We commonly encounter the problem of identifying an optimally weight adjusted version of the empirical distribution of observed data, adhering to predefined constraints on the weig…
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors
Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1
Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named -TS, where we use a…
Memory Efficient And Minimax Distribution Estimation Under Wasserstein Distance Using Bayesian Histograms
Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya +1
We study Bayesian histograms for distribution estimation on under the Wasserstein distance in the i.i.d sampling regime. We newly show that when…
On the Convergence of Coordinate Ascent Variational Inference
Anirban Bhattacharya, Debdeep Pati, Yun Yang
As a computational alternative to Markov chain Monte Carlo approaches, variational inference (VI) is becoming more and more popular for approximating intractable posterior distribu…
Fair Clustering via Hierarchical Fair-Dirichlet Process
Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised mac…
EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations
Yuhao Zhong, Anirban Bhattacharya, Satish Bukkapatnam
We propose EBLIME to explain black-box machine learning models and obtain the distribution of feature importance using Bayesian ridge regression models. We provide mathematical exp…
Robust probabilistic inference via a constrained transport metric
Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati
Flexible Bayesian models are typically constructed using limits of large parametric models with a multitude of parameters that are often uninterpretable. In this article, we offer…