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20092026
most citedA geometric alternative to Nesterov's accelerated gradient descent

85 citations · 237 across the 30 of their papers we have counts for

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Showing 2018Show all

7 papers · 1 filter

math.CO2018

Simple Graph Density Inequalities with no Sum of Squares Proofs

Grigoriy Blekherman, Annie Raymond, Mohit Singh +1

Establishing inequalities among graph densities is a central pursuit in extremal combinatorics. A standard tool to certify the nonnegativity of a graph density expression is to wri…

cs.DS2018

Sticky Brownian Rounding and its Applications to Constraint Satisfaction Problems

Sepehr Abbasi-Zadeh, Nikhil Bansal, Guru Guruganesh +3

Semidefinite programming is a powerful tool in the design and analysis of approximation algorithms for combinatorial optimization problems. In particular, the random hyperplane rou…

cs.LG2018★ 77 cited

The Price of Fair PCA: One Extra Dimension

Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern +2

We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We…

cs.DS2018

Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency

Sebastian Perez-Salazar, Ishai Menache, Mohit Singh +1

Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, amon…

cs.DS2018★ 1 cited

Efficient algorithms for robust submodular maximization under matroid constraints

Sebastian Pokutta, Mohit Singh, Alfredo Torrico

In this work, we consider robust submodular maximization with matroid constraints. We give an efficient bi-criteria approximation algorithm that outputs a small family of feasible…

stat.ML2018★ 1 cited

Approximation Algorithms for D-optimal Design

Mohit Singh, Weijun Xie

Experimental design is a classical statistics problem and its aim is to estimate an unknown -dimensional vector from linear measurements where a Gaussian noise is introduced…