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Mehtaab Sawhney

dblp:175/1695 · also Mehtaab S. Sawhney · DBLP profile ↗
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11ranked-venue papers
1as first author
10since 2021 · last 2025
0009-0002-3238-7134ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 11 · 1 first-author · 10 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
9 papers
Combinatorics and discrete mathematics · 24% Approximation and online algorithms · 23% Computational complexity · 18%
Network and information security
1 paper
Privacy and data protection · 100%
Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 21 heaviest of 25, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Approximation and online algorithms
online algorithms
1.832024
Online Edge Coloring via Tree Recurrences and Correlation Decay · SIAM J. Comput. 2024
Online edge coloring via tree recurrences and correlation decay · STOC 2022
Discrepancy minimization via a self-balancing walk · STOC 2021
Approximation and online algorithms › online algorithms › online graph algorithms
online edge coloring
1.322024
Online Edge Coloring via Tree Recurrences and Correlation Decay · SIAM J. Comput. 2024
Online edge coloring via tree recurrences and correlation decay · STOC 2022
Combinatorics and discrete mathematics › discrepancy theory
discrepancy minimization
1.222023
Spencer's theorem in nearly input-sparsity time · SODA 2023
Discrepancy minimization via a self-balancing walk · STOC 2021
Combinatorics and discrete mathematics
discrepancy theory
1.222023
Spencer's theorem in nearly input-sparsity time · SODA 2023
Discrepancy minimization via a self-balancing walk · STOC 2021
Algorithms and data structures › randomized algorithms › sampling
approximate sampling
1.122022
Approximate counting and sampling via local central limit theorems · STOC 2022
Perfectly sampling k ≥ (8/3 + o(1))Δ-colorings in graphs · STOC 2021
Combinatorics and discrete mathematics
additive combinatorics
0.912025
Quasipolynomial Bounds for the Corners Theorem · FOCS 2025
Computational complexity
communication complexity
0.912025
Quasipolynomial Bounds for the Corners Theorem · FOCS 2025
Computational complexity › communication complexity › multiparty communication complexity
number-on-forehead model
0.912025
Quasipolynomial Bounds for the Corners Theorem · FOCS 2025
Graph algorithms and graph theory › graph coloring
edge coloring
0.812024
Online Edge Coloring via Tree Recurrences and Correlation Decay · SIAM J. Comput. 2024
Machine learning › Learning theory › online learning
perceptron
0.712023
Distribution of the threshold for the symmetric perceptron · FOCS 2023
Privacy and data protection
differential privacy
0.712023
Optimal Minimization of the Covariance Loss · IEEE Trans. Inf. Theory 2023
Privacy and data protection › differential privacy
synthetic data generation
0.712023
Optimal Minimization of the Covariance Loss · IEEE Trans. Inf. Theory 2023
Algorithms and data structures › numerical linear algebra › randomized numerical linear algebra
input-sparsity time algorithms
0.712023
Spencer's theorem in nearly input-sparsity time · SODA 2023
Computational complexity › counting problems
approximate counting
0.612022
Approximate counting and sampling via local central limit theorems · STOC 2022
Graph algorithms and graph theory › graph sampling
independent set sampling
0.612022
Approximate counting and sampling via local central limit theorems · STOC 2022
Algorithmic game theory and mechanism design
matching
0.612022
Online edge coloring via tree recurrences and correlation decay · STOC 2022
Computational complexity › counting problems
perfect matching counting
0.612022
Approximate counting and sampling via local central limit theorems · STOC 2022
Algorithms and data structures › randomized algorithms › sampling
markov chain monte carlo
0.512021
Perfectly sampling k ≥ (8/3 + o(1))Δ-colorings in graphs · STOC 2021
Approximation and online algorithms › online algorithms › online combinatorial optimization
online vector balancing
0.512021
Discrepancy minimization via a self-balancing walk · STOC 2021
Graph algorithms and graph theory › graph coloring
proper colorings
0.512021
Perfectly sampling k ≥ (8/3 + o(1))Δ-colorings in graphs · STOC 2021
Information theory › information measures › multiterminal information measures
common information
0.212023
Optimal Minimization of the Covariance Loss · IEEE Trans. Inf. Theory 2023

Methods — techniques the papers use, named apart from their topics

tree recurrences · 1.3correlation decay · 1.3pinning lemma · 1.3algorithmic construction · 1.3matching reduction · 0.8discrepancy theory · 0.7local central limit theorem · 0.6FPTAS · 0.6self-balancing walk · 0.5komlós conjecture · 0.5
YearPublicationVenuePosition
2025 Quasipolynomial Bounds for the Corners Theorem
abstract
Let G be a finite abelian group and A be a subset of $G \times G$ which is corner-free, meaning that there are no $x, y \in G$ and $d \in G \backslash\{0\}$ such that $(x, y),(x+d, y),(x, y+d) \in A$. We prove that \begin{equation*}|A| \leq|G|^{2} \cdot \exp \left(-(\log |G|)^{\Omega{1}}\right)\end{equation*}As a consequence, we obtain polynomial (in the input length) lower bounds on the non-deterministic communication complexity of Exactly-N in the 3-player Number-on-Forehead model. We also obtain the first “reasonable” lower bounds on the coloring version of the 3 -dimensional corners problem, as well as on the non-deterministic communication complexity of Exactly-N in the 4-player Number-on-Forehead model. This is an extended abstract. The full version of the paper can be found at https://arxiv.org/abs/2504.07006.
Michael Jaber, Yang P. Liu, Shachar Lovett, Anthony Ostuni, Mehtaab Sawhney
FOCS5
2024 Online Edge Coloring via Tree Recurrences and Correlation Decay
abstract
Abstract. We give an online algorithm that with high probability computes a [Formula: see text] edge coloring on a graph [Formula: see text] with maximum degree [Formula: see text] under online edge arrivals against oblivious adversaries, making first progress on the conjecture of Bar-Noy, Motwani, and Naor in this general setting. Our algorithm is based on reducing to a matching problem on locally treelike graphs, and then applying a tree recurrence based approach for arguing correlation decay.
Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski
SIAM J. Comput.4
2023 Distribution of the threshold for the symmetric perceptron
abstract
We derive an explicit distribution for the threshold sequence of the symmetric binary perceptron with Gaussian disorder, proving that the critical window is of constant width.
Ashwin Sah, Mehtaab Sawhney
FOCS2
2023 Spencer's theorem in nearly input-sparsity time
abstract
A celebrated theorem of Spencer states that for every set system S1,…, Sm ⊆ [n], there is a coloring of the ground set with {±1} with discrepancy . We provide an algorithm to find such a coloring in near input-sparsity time Õ(n + Σi=1m|Si|)
Vishesh Jain, Ashwin Sah, Mehtaab Sawhney
SODA3
2023 Optimal Minimization of the Covariance Loss
abstract
Let$X$be a random vector valued in$\mathbb {R}^{m}$such that$\|X\|_{2} \le 1$almost surely. For every$k\ge 3$, we show that there exists a sigma algebra$\mathcal {F}$generated by a partition of$\mathbb {R}^{m}$into$k$sets such that$\|\mathrm {Cov}(X) - \mathrm {Cov}(\mathbb {E}[X\mid \mathcal {F}]) \|_{\mathrm {F}} \lesssim \frac {1}{\sqrt {\log {k}}}$. This is optimal up to the implicit constant and improves on a previous bound due to Boedihardjo, Strohmer, and Vershynin. Our proof provides an efficient algorithm for constructing$\mathcal {F}$and leads to improved accuracy guarantees for$k$-anonymous or differentially private synthetic data. We also establish a connection between the above problem of minimizing the covariance loss and the pinning lemma from statistical physics, providing an alternate (and much simpler) algorithmic proof in the important case when$X \in \{\pm 1\}^{m}/\sqrt {m}$almost surely.
Vishesh Jain, Ashwin Sah, Mehtaab Sawhney
IEEE Trans. Inf. Theory3
2022 A Gaussian Fixed Point Random Walk
abstract
In this note, we design a discrete random walk on the real line which takes steps 0,±1 (and one with steps in {±1,2}) where at least 96% of the signs are ±1 in expectation, and which has 𝒩(0,1) as a stationary distribution. As an immediate corollary, we obtain an online version of Banaszczyk’s discrepancy result for partial colorings and ±1,2 signings. Additionally, we recover linear time algorithms for logarithmic bounds for the Komlós conjecture in an oblivious online setting.
Yang P. Liu, Ashwin Sah, Mehtaab Sawhney
ITCS3
2022 Approximate counting and sampling via local central limit theorems
abstract
We give an FPTAS for computing the number of matchings of size k in a graph G of maximum degree Δ on n vertices, for all k ≤ (1−δ)m*(G), where δ>0 is fixed and m*(G) is the matching number of G, and an FPTAS for the number of independent sets of size k ≤ (1−δ) αc(Δ) n, where αc(Δ) is the NP-hardness threshold for this problem. We also provide quasi-linear time randomized algorithms to approximately sample from the uniform distribution on matchings of size k ≤ (1−δ)m*(G) and independent sets of size k ≤ (1−δ)αc(Δ)n.
Vishesh Jain, Will Perkins 0001, Ashwin Sah, Mehtaab Sawhney
STOC4
2022 Online edge coloring via tree recurrences and correlation decay
abstract
We give an online algorithm that with high probability computes a (e/e−1 + o(1))Δ edge coloring on a graph G with maximum degree Δ = ω(logn) under online edge arrivals against oblivious adversaries, making first progress on the conjecture of Bar-Noy, Motwani, and Naor in this general setting. Our algorithm is based on reducing to a matching problem on locally treelike graphs, and then applying a tree recurrences based approach for arguing correlation decay.
Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski
STOC4
2021 Discrepancy minimization via a self-balancing walk
abstract
We study discrepancy minimization for vectors in ℝn under various settings. The main result is the analysis of a new simple random process in high dimensions through a comparison argument. As corollaries, we obtain bounds which are tight up to logarithmic factors for online vector balancing against oblivious adversaries, resolving several questions posed by Bansal, Jiang, Singla, and Sinha (STOC 2020), as well as a linear time algorithm for logarithmic bounds for the Komlós conjecture.
Ryan Alweiss, Yang P. Liu, Mehtaab Sawhney
STOC3
2021 Perfectly sampling k ≥ (8/3 + o(1))Δ-colorings in graphs
abstract
We present a randomized algorithm which takes as input an undirected graph G on n vertices with maximum degree Δ, and a number of colors k ≥ (8/3 + oΔ(1))Δ, and returns – in expected time Õ(nΔ2logk) – a proper k-coloring of G distributed perfectly uniformly on the set of all proper k-colorings of G. Notably, our sampler breaks the barrier at k = 3Δ encountered in recent work of Bhandari and Chakraborty [STOC 2020]. We also discuss how our methods may be modified to relax the restriction on k to k ≥ (8/3 − є0)Δ for an absolute constant є0 > 0.
Vishesh Jain, Ashwin Sah, Mehtaab Sawhney
STOC3
2018 On Symmetric But Not Cyclotomic Numerical Semigroups
abstract
A numerical semigroup $S$ is called cyclotomic if its corresponding numerical semigroup polynomial, $P_S(x)=(1-x)\sum_{s\in S}x^s$, is expressible as a product of cyclotomic polynomials. Ciolan, García-Sánchez, and Moree [ SIAM J. Discrete Math., 30 (2016), pp. 650--668] conjectured that, for every embedding dimension at least 4, there exists a numerical semigroup which is symmetric but not cyclotomic. We prove this conjecture by giving an infinite class of numerical semigroup families $S_{n, t}$, which, for every fixed $t$, are symmetric but not cyclotomic when $n\ge \max\big(8(t+1)^3,40(t+2)\big)$. We also verify through a finite case check that the numerical semigroup families $S_{n, 0}$ and $S_{n, 1}$ yield noncyclotomic numerical semigroups for every embedding dimension at least 4.
Mehtaab Sawhney, David Stoner
SIAM J. Discret. Math.1