35 citations · 82 across the 23 of their papers we have counts for
7 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…
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…
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…
Active causal structure learning with advice
Davin Choo, Themis Gouleakis, Arnab Bhattacharyya
We introduce the problem of active causal structure learning with advice. In the typical well-studied setting, the learning algorithm is given the essential graph for the observati…
Communication and Memory Efficient Testing of Discrete Distributions
Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane +1
We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize…
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos
We study the problem of {\em distribution-independent} PAC learning of halfspaces in the presence of Massart noise. Specifically, we are given a set of labeled examples $(\mathbf{x…