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20122026
most citedFrequentist coverage and sup-norm convergence rate in Gaussian process regression

36 citations · 67 across the 26 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

stat.ME2023

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…

stat.ML2023

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…

stat.ML2023

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…

math.ST20231 cited

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…

stat.ML2023

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…

stat.ML20231 cited

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…