68 citations · 353 across the 57 of their papers we have counts for
8 papers · 1 filter
Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling
Prateek Jaiswal, Debdeep Pati, Anirban Bhattacharya +1
We study a variant of the Thompson Sampling (TS) algorithm, called -TS, for solving stochastic generalized linear bandit problems. Existing analyses of TS require inflating the…
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…
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…
Structured Optimal Variational Inference for Dynamic Latent Space Models
Peng Zhao, Anirban Bhattacharya, Debdeep Pati +1
We consider a latent space model for dynamic networks, where our objective is to estimate the pairwise inner products plus the intercept of the latent positions. To balance posteri…
Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting
Biraj Subhra Guha, Anirban Bhattacharya, Debdeep Pati
We provide statistical guarantees for Bayesian variational boosting by proposing a novel small bandwidth Gaussian mixture variational family. We employ a functional version of Fran…