8 papers · 1 filter
Product distribution learning with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
Given i.i.d.~samples from an unknown distribution , the goal of distribution learning is to recover the parameters of a distribution that is close to . When belongs to th…
Learning High-dimensional Gaussians from Censored Data
Arnab Bhattacharyya, Constantinos Daskalakis, Themis Gouleakis +1
We provide efficient algorithms for the problem of distribution learning from high-dimensional Gaussian data where in each sample, some of the variable values are missing. We suppo…
Efficient, Low-Regret, Online Reinforcement Learning for Linear MDPs
Philips George John, Arnab Bhattacharyya, Silviu Maniu +2
Reinforcement learning algorithms are usually stated without theoretical guarantees regarding their performance. Recently, Jin, Yang, Wang, and Jordan (COLT 2020) showed a polynomi…
Learning multivariate Gaussians with imperfect advice
Arnab Bhattacharyya, Davin Choo, Philips George John +1
We revisit the problem of distribution learning within the framework of learning-augmented algorithms. In this setting, we explore the scenario where a probability distribution is…
Learnability of Parameter-Bounded Bayes Nets
Arnab Bhattacharyya, Davin Choo, Sutanu Gayen +1
Bayes nets are extensively used in practice to efficiently represent joint probability distributions over a set of random variables and capture dependency relations. In a seminal p…
Online bipartite matching with imperfect advice
Davin Choo, Themis Gouleakis, Chun Kai Ling +1
We study the problem of online unweighted bipartite matching with offline vertices and online vertices where one wishes to be competitive against the optimal offline algori…