5 citations · 12 across the 22 of their papers we have counts for
30 papers
Sampling from the Sherrington-Kirkpatrick Model up to Criticality
Hang Du, Brice Huang, Sidhanth Mohanty +1
We prove that the simulated annealing algorithm efficiently samples from the Sherrington-Kirkpatrick model at inverse temperature to an arbitrary inverse polynomial accuracy…
Rigorous Implications of the Low-Degree Heuristic
Jun-Ting Hsieh, Daniel M. Kane, Pravesh K. Kothari +3
Over the past decade, the low-degree heuristic has been used to estimate the algorithmic thresholds for a wide range of average-case planted vs null distinguishing problems. Such r…
Explicit Almost-Optimal -Balanced Codes via Free Expander Walks
Jun-Ting Hsieh, Sidhanth Mohanty, Rachel Yun Zhang
We study the problem of constructing explicit codes whose rate and distance match the Gilbert-Varshamov bound in the low-rate, high-distance regime. In 2017, Ta-Shma gave an explic…
Eigenvalue Bounds for Random Matrices via Zerofreeness
Sidhanth Mohanty, Amit Rajaraman
We introduce a new technique to prove bounds for the spectral radius of a random matrix, based on using Jensen's formula to establish the zerofreeness of the associated characteris…
Sparsifying Cayley Graphs on Every Group
Jun-Ting Hsieh, Daniel Z. Lee, Sidhanth Mohanty +2
A classic result in graph theory, due to Batson, Spielman, and Srivastava (STOC 2009) shows that every graph admits a cut (or spectral) sparsifier which prese…
Explicit Lossless Vertex Expanders
Jun-Ting Hsieh, Alexander Lubotzky, Sidhanth Mohanty +2
We give the first construction of explicit constant-degree lossless vertex expanders. Specifically, for any and sufficiently large , we give an explicit constr…
Weak Poincaré Inequalities, Simulated Annealing, and Sampling from Spherical Spin Glasses
Brice Huang, Sidhanth Mohanty, Amit Rajaraman +1
There has been a recent surge of powerful tools to show rapid mixing of Markov chains, via functional inequalities such as Poincaré inequalities. In many situations, Markov chains…
Explicit Two-Sided Vertex Expanders Beyond the Spectral Barrier
Jun-Ting Hsieh, Ting-Chun Lin, Sidhanth Mohanty +2
We construct the first explicit two-sided vertex expanders that bypass the spectral barrier. Previously, the strongest known explicit vertex expanders were given by -regular Ram…
Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
Kuikui Liu, Sidhanth Mohanty, Prasad Raghavendra +2
Many natural Markov chains fail to mix to their stationary distribution in polynomially many steps. Often, this slow mixing is inevitable since it is computationally intractable to…
Fast Mixing in Sparse Random Ising Models
Kuikui Liu, Sidhanth Mohanty, Amit Rajaraman +1
Motivated by the community detection problem in Bayesian inference, as well as the recent explosion of interest in spin glasses from statistical physics, we study the classical Gla…
Robust recovery for stochastic block models, simplified and generalized
Sidhanth Mohanty, Prasad Raghavendra, David X. Wu
We study the problem of : efficiently recovering communities in sparse stochastic block models in the presence of adversarial corruptions. In th…
Small Even Covers, Locally Decodable Codes and Restricted Subgraphs of Edge-Colored Kikuchi Graphs
Jun-Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty +2
Given a -uniform hypergraph on vertices, an even cover in is a collection of hyperedges that touch each vertex an even number of times. Even covers are a generalizat…
Explicit two-sided unique-neighbor expanders
Jun-Ting Hsieh, Theo McKenzie, Sidhanth Mohanty +1
We study the problem of constructing explicit sparse graphs that exhibit strong vertex expansion. Our main result is the first two-sided construction of imbalanced unique-neighbor…
Local and global expansion in random geometric graphs
Siqi Liu, Sidhanth Mohanty, Tselil Schramm +1
Consider a random geometric 2-dimensional simplicial complex sampled as follows: first, sample vectors uniformly at random on $\m…
A simple and sharper proof of the hypergraph Moore bound
Jun-Ting Hsieh, Pravesh K. Kothari, Sidhanth Mohanty
The hypergraph Moore bound is an elegant statement that characterizes the extremal trade-off between the girth - the number of hyperedges in the smallest cycle or even cover (a sub…
Testing thresholds for high-dimensional sparse random geometric graphs
Siqi Liu, Sidhanth Mohanty, Tselil Schramm +1
In the random geometric graph model , we identify each of our vertices with an independently and uniformly sampled vector from the -dimensional unit sph…
Many nodal domains in random regular graphs
Shirshendu Ganguly, Theo McKenzie, Sidhanth Mohanty +1
Let be a random -regular graph. We prove that for every constant , with high probability every eigenvector of the adjacency matrix of with eigenvalue less than $-2…
Certifying solution geometry in random CSPs: counts, clusters and balance
Jun-Ting Hsieh, Sidhanth Mohanty, Jeff Xu
An active topic in the study of random constraint satisfaction problems (CSPs) is the geometry of the space of satisfying or almost satisfying assignments as the function of the de…
On statistical inference when fixed points of belief propagation are unstable
Siqi Liu, Sidhanth Mohanty, Prasad Raghavendra
Many statistical inference problems correspond to recovering the values of a set of hidden variables from sparse observations on them. For instance, in a planted constraint satisfa…
High-girth near-Ramanujan graphs with lossy vertex expansion
Theo McKenzie, Sidhanth Mohanty
Kahale proved that linear sized sets in -regular Ramanujan graphs have vertex expansion and complemented this with construction of near-Ramanujan graphs with v…
List Decodable Mean Estimation in Nearly Linear Time
Yeshwanth Cherapanamjeri, Sidhanth Mohanty, Morris Yau
Learning from data in the presence of outliers is a fundamental problem in statistics. Until recently, no computationally efficient algorithms were known to compute the mean of a h…
Pseudo-deterministic Streaming
Shafi Goldwasser, Ofer Grossman, Sidhanth Mohanty +1
A pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple times on the same input, with high probability outputs the same result on all executions. Clas…
Lifting Sum-of-Squares Lower Bounds: Degree- to Degree-
Sidhanth Mohanty, Prasad Raghavendra, Jeff Xu
The degree- Sum-of-Squares (SoS) SDP relaxation is a powerful algorithm that captures the best known polynomial time algorithms for a broad range of problems including MaxCut, S…
Local Statistics, Semidefinite Programming, and Community Detection
Jess Banks, Sidhanth Mohanty, Prasad Raghavendra
We propose a new hierarchy of semidefinite programming relaxations for inference problems. As test cases, we consider the problem of community detection in block models. The vertic…