activity
20172026
most citedFlow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

70 citations · 129 across the 18 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.IT2025

From Random to Explicit via Subspace Designs With Applications to Local Properties and Matroids

Joshua Brakensiek, Yeyuan Chen, Manik Dhar +1

In coding theory, a common question is to understand the threshold rates of various local properties of codes, such as their list decodability and list recoverability. A recent wor…

cs.IT2025

Combinatorial Bounds for List Recovery via Discrete Brascamp--Lieb Inequalities

Joshua Brakensiek, Yeyuan Chen, Manik Dhar +1

In coding theory, the problem of list recovery asks one to find all codewords of a given code which such that at least fraction of the symbols of lie in some pred…

math.CO2025

Iterated sumset expansion in

Manik Dhar, Sammy Luo

Given a set , what conditions does one need to guarantee that iterated sumsets of the form expand quickly (say, within terms) to the…

cs.IT2025

Improved Constructions and Lower Bounds for Maximally Recoverable Grid Codes

Joshua Brakensiek, Manik Dhar, Sivakanth Gopi

In this paper, we continue the study of Maximally Recoverable (MR) Grid Codes initiated by Gopalan et al. [SODA 2017]. More precisely, we study codes over an grid topo…

cs.IT2025

Unique Decoding of Reed-Solomon and Related Codes for Semi-Adversarial Errors

Joshua Brakensiek, Yeyuan Chen, Manik Dhar +1

Motivated by recent developments in coding theory, particular in list-decoding, we introduce a new error model which we call semi-adversarial errors. This error model bridges betwe…

math.CO2025

Covering points with planes

Hailong Dao, Manik Dhar, Izabella Łaba +1

Suppose that each proper subset of a set of points in a vector space is contained in the union of planes of specified dimensions, but itself is not contained in any such un…