Aaron Sidford

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MIT ∙ Microsoft ∙ Stanford University

Assistant professor of Management Science and Engineering and, by courtesy, of Computer Science at Stanford University.

Large-Scale Methods for Distributionally Robust Optimization

We propose and analyze algorithms for distributionally robust optimizati...
10/12/2020 ∙ by Daniel Lévy, et al. ∙ 0 ∙ share

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Coordinate Methods for Matrix Games

We develop primal-dual coordinate methods for solving bilinear saddle-po...
09/17/2020 ∙ by Yair Carmon, et al. ∙ 0 ∙ share

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Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs

We present an Õ(m+n^1.5)-time randomized algorithm for maximum cardinali...
09/03/2020 ∙ by Jan van den Brand, et al. ∙ 0 ∙ share

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Efficiently Solving MDPs with Stochastic Mirror Descent

We present a unified framework based on primal-dual stochastic mirror de...
08/28/2020 ∙ by Yujia Jin, et al. ∙ 0 ∙ share

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Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise

Classical iterative algorithms for linear system solving and regression ...
08/04/2020 ∙ by Jerry Li, et al. ∙ 0 ∙ share

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Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary

Designing dynamic graph algorithms against an adaptive adversary is a ma...
04/17/2020 ∙ by Aaron Bernstein, et al. ∙ 0 ∙ share

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The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood

In this paper we consider the problem of computing the likelihood of the...
04/06/2020 ∙ by Nima Anari, et al. ∙ 8 ∙ share

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Faster Divergence Maximization for Faster Maximum Flow

In this paper we provide an algorithm which given any m-edge n-vertex di...
03/19/2020 ∙ by Yang P. Liu, et al. ∙ 0 ∙ share

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Acceleration with a Ball Optimization Oracle

Consider an oracle which takes a point x and returns the minimizer of a ...
03/18/2020 ∙ by Yair Carmon, et al. ∙ 0 ∙ share

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A General Framework for Symmetric Property Estimation

In this paper we provide a general framework for estimating symmetric pr...
03/02/2020 ∙ by Moses Charikar, et al. ∙ 0 ∙ share

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Solving Tall Dense Linear Programs in Nearly Linear Time

In this paper we provide an Õ(nd+d^3) time randomized algorithm for solv...
02/06/2020 ∙ by Jan van den Brand, et al. ∙ 0 ∙ share

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High-precision Estimation of Random Walks in Small Space

In this paper, we provide a deterministic Õ(log N)-space algorithm for e...
12/10/2019 ∙ by AmirMahdi Ahmadinejad, et al. ∙ 0 ∙ share

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Faster Matroid Intersection

In this paper we consider the classic matroid intersection problem: give...
11/25/2019 ∙ by Deeparnab Chakrabarty, et al. ∙ 0 ∙ share

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Faster Energy Maximization for Faster Maximum Flow

In this paper we provide an algorithm which given any m-edge n-vertex di...
10/31/2019 ∙ by Yang P. Liu, et al. ∙ 0 ∙ share

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Solving Linear Programs with Sqrt(rank) Linear System Solves

We present an algorithm that given a linear program with n variables, m ...
10/17/2019 ∙ by Yin Tat Lee, et al. ∙ 0 ∙ share

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Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG

Given a data matrix A∈R^n × d, principal component projection (PCP) and ...
10/15/2019 ∙ by Yujia Jin, et al. ∙ 0 ∙ share

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Near-optimal Approximate Discrete and Continuous Submodular Function Minimization

In this paper we provide improved running times and oracle complexities ...
08/31/2019 ∙ by Brian Axelrod, et al. ∙ 0 ∙ share

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Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity

In this paper, we settle the sampling complexity of solving discounted t...
08/29/2019 ∙ by Aaron Sidford, et al. ∙ 12 ∙ share

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Improved Girth Approximation and Roundtrip Spanners

In this paper we provide improved algorithms for approximating the girth...
07/25/2019 ∙ by Shiri Chechik, et al. ∙ 0 ∙ share

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Variance Reduction for Matrix Games

We present a randomized primal-dual algorithm that solves the problem _x...
07/03/2019 ∙ by Yair Carmon, et al. ∙ 0 ∙ share

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Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond

In this paper, we provide near-optimal accelerated first-order methods f...
06/27/2019 ∙ by Oliver Hinder, et al. ∙ 0 ∙ share

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Complexity of Highly Parallel Non-Smooth Convex Optimization

A landmark result of non-smooth convex optimization is that gradient des...
06/25/2019 ∙ by Sébastien Bubeck, et al. ∙ 0 ∙ share

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A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport

Optimal transportation, or computing the Wasserstein or “earth mover's” ...
06/03/2019 ∙ by Arun Jambulapati, et al. ∙ 0 ∙ share

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Parallel Reachability in Almost Linear Work and Square Root Depth

In this paper we provide a parallel algorithm that given any n-node m-ed...
05/21/2019 ∙ by Arun Jambulapati, et al. ∙ 0 ∙ share

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Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation

Estimating symmetric properties of a distribution, e.g. support size, co...
05/21/2019 ∙ by Moses Charikar, et al. ∙ 0 ∙ share

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Memory-Sample Tradeoffs for Linear Regression with Small Error

We consider the problem of performing linear regression over a stream of...
04/18/2019 ∙ by Vatsal Sharan, et al. ∙ 0 ∙ share

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Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space

In this paper we consider the problem of computing spectral approximatio...
03/28/2019 ∙ by Michael Kapralov, et al. ∙ 0 ∙ share

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Deterministic Approximation of Random Walks in Small Space

We give a deterministic, nearly logarithmic-space algorithm that given a...
03/15/2019 ∙ by Jack Murtagh, et al. ∙ 0 ∙ share

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A Rank-1 Sketch for Matrix Multiplicative Weights

We show that a simple randomized sketch of the matrix multiplicative wei...
03/07/2019 ∙ by Yair Carmon, et al. ∙ 0 ∙ share

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Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices

In this paper we show how to recover a spectral approximations to broad ...
12/15/2018 ∙ by Arun Jambulapati, et al. ∙ 0 ∙ share

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Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression

In this paper, we obtain improved running times for regression and top e...
11/27/2018 ∙ by Neha Gupta, et al. ∙ 0 ∙ share

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Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations

We show how to solve directed Laplacian systems in nearly-linear time. G...
11/26/2018 ∙ by Michael B. Cohen, et al. ∙ 0 ∙ share

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Towards Optimal Running Times for Optimal Transport

In this work, we provide faster algorithms for approximating the optimal...
10/17/2018 ∙ by Jose Blanchet, et al. ∙ 0 ∙ share

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Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications

In this paper we provide nearly linear time algorithms for several probl...
10/04/2018 ∙ by AmirMahdi Ahmadinejad, et al. ∙ 0 ∙ share

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Coordinate Methods for Accelerating ℓ_∞ Regression and Faster Approximate Maximum Flow

In this paper we provide faster algorithms for approximately solving ℓ_∞...
08/03/2018 ∙ by Aaron Sidford, et al. ∙ 0 ∙ share

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Leverage Score Sampling for Faster Accelerated Regression and ERM

Given a matrix A∈R^n× d and a vector b ∈R^d, we show how to compute an ϵ...
11/22/2017 ∙ by Naman Agarwal, et al. ∙ 0 ∙ share

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Efficient O(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse

In this paper we consider the problem of efficiently computing ϵ-sketche...
11/02/2017 ∙ by Arun Jambulapati, et al. ∙ 0 ∙ share

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A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares)

This work provides a simplified proof of the statistical minimax optimal...
10/25/2017 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space

We give a deterministic Õ( n)-space algorithm for approximately solving ...
08/15/2017 ∙ by Jack Murtagh, et al. ∙ 0 ∙ share

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Efficient Convex Optimization with Membership Oracles

We consider the problem of minimizing a convex function over a convex se...
06/22/2017 ∙ by Yin Tat Lee, et al. ∙ 0 ∙ share

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Accelerating Stochastic Gradient Descent

There is widespread sentiment that it is not possible to effectively uti...
04/26/2017 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging

This work characterizes the benefits of averaging techniques widely used...
10/12/2016 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis

This paper considers the problem of canonical-correlation analysis (CCA)...
04/13/2016 ∙ by Rong Ge, et al. ∙ 0 ∙ share

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Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm

This work provides improved guarantees for streaming principle component...
02/22/2016 ∙ by Prateek Jain, et al. ∙ 0 ∙ share

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Principal Component Projection Without Principal Component Analysis

We show how to efficiently project a vector onto the top principal compo...
02/22/2016 ∙ by Roy Frostig, et al. ∙ 0 ∙ share

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Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

We develop a family of accelerated stochastic algorithms that minimize s...
06/24/2015 ∙ by Roy Frostig, et al. ∙ 0 ∙ share

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Competing with the Empirical Risk Minimizer in a Single Pass

In many estimation problems, e.g. linear and logistic regression, we wis...
12/20/2014 ∙ by Roy Frostig, et al. ∙ 0 ∙ share

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Aaron Sidford

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