activity
20152024
most citedLocal algorithms and the failure of log-depth quantum advantage on sparse random CSPs

3 citations · 4 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ML2024

Top- ranking with a monotone adversary

Yuepeng Yang, Antares Chen, Lorenzo Orecchia +1

In this paper, we address the top- ranking problem with a monotone adversary. We consider the scenario where a comparison graph is randomly generated and the adversary is allowe…

quant-ph2023★ 3 cited

Local algorithms and the failure of log-depth quantum advantage on sparse random CSPs

Antares Chen, Neng Huang, Kunal Marwaha

We construct and analyze a message-passing algorithm for random constraint satisfaction problems (CSPs) at large clause density, generalizing work of El Alaoui, Montanari, and Sell…

cs.DS2023

Hypergraph Diffusions and Resolvents for Norm-Based Hypergraph Laplacians

Konstantinos Ameranis, Antares Chen, Adela DePavia +2

The development of simple and fast hypergraph spectral methods has been hindered by the lack of numerical algorithms for simulating heat diffusions and computing fundamental object…

cs.DS2023

Submodular Hypergraph Partitioning: Metric Relaxations and Fast Algorithms via an Improved Cut-Matching Game

Antares Chen, Lorenzo Orecchia, Erasmo Tani

Despite there being significant work on developing spectral, and metric embedding based approximation algorithms for hypergraph generalizations of conductance, little is known rega…

cs.DS2020

Cut Sparsification of the Clique Beyond the Ramanujan Bound: A Separation of Cut Versus Spectral Sparsification

Antares Chen, Jonathan Shi, Luca Trevisan

We prove that a random -regular graph, with high probability, is a cut sparsifier of the clique with approximation error at most $\left(2\sqrt{\frac 2 π} + o_{n,d}(1)\right)/\sq…

cs.DS2015★ 1 cited

Partial resampling to approximate covering integer programs

Antares Chen, David G. Harris, Aravind Srinivasan

We consider column-sparse covering integer programs, a generalization of set cover, which have a long line of research of (randomized) approximation algorithms. We develop a new ro…