15 citations · 45 across the 10 of their papers we have counts for
6 papers · 1 filter
Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data
Yabo Niu, Yang Ni, Debdeep Pati +1
In a traditional Gaussian graphical model, data homogeneity is routinely assumed with no extra variables affecting the conditional independence. In modern genomic datasets, there i…
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…
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…
An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling
Sutanoy Dasgupta, Peng Zhao, Jacob Helwig +3
Gaussian graphical models typically assume a homogeneous structure across all subjects, which is often restrictive in applications. In this article, we propose a weighted pseudo-li…