Publications

Conference Publications

2023

Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling

With Adam Bouland, Yosheb Getachew, Yujia Jin, and Kevin Tian:
In International Conference on Machine Learning (ICML 2023) (arXiv)

Moments, Random Walks, and Limits for Spectrum Approximation

With Yujia Jin, Christopher Musco, and Apoorv Vikram Singh
In Conference on Learning Theory (COLT 2023) (arXiv)

Semi-Random Sparse Recovery in Nearly-Linear Time

With Jonathan Kelner, Jerry Li, Allen X Liu, and Kevin Tian
In Conference on Learning Theory (COLT 2023) (arXiv)

Dynamic Maxflow via Dynamic Interior Point Methods

With Jan van den Brand and Yang P. Liu
In Symposium on Theory of Computing (STOC 2023) (arXiv)

Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification

With Arun Jambulapati and Yang P. Liu
In Symposium on Theory of Computing (STOC 2023) (arXiv)

Improved girth approximation in weighted undirected graphs

With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick
In Symposium on Discrete Algorithms (SODA 2023)

The Complexity of Infinite-Horizon General-Sum Stochastic Games

With Yujia Jin and Vidya Muthukumar
In Innovations in Theoretical Computer Science (ITCS 2023) (arXiv)

2022

Optimal and Adaptive Monteiro-Svaiter Acceleration

With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin
In Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv)

On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood

With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur
In Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv)

Improved Lower Bounds for Submodular Function Minimization

With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang
In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv)

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization

With Yair Carmon, Arun Jambulapati, and Yujia Jin
In International Conference on Machine Learning (ICML 2022) (arXiv)

Efficient Convex Optimization Requires Superlinear Memory

With Annie Marsden, Vatsal Sharan, and Gregory Valiant
In Conference on Learning Theory (COLT 2022)
Best paper award (arXiv)

Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method

With Yujia Jin and Kevin Tian
In Conference on Learning Theory (COLT 2022) (arXiv)

Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales

With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan
In Conference on Learning Theory (COLT 2022) (arXiv)

Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching

With Arun Jambulapati, Yujia Jin, and Kevin Tian
In International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv)

Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary

With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun
In International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv)

Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers

With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng
In Symposium on Theory of Computing (STOC 2022) (arXiv)

Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space

With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian
In Symposium on Discrete Algorithms (SODA 2022) (arXiv)

Algorithmic trade-offs for girth approximation in undirected graphs

With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick
In Symposium on Discrete Algorithms (SODA 2022)

Computing Lewis Weights to High Precision

With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan
In Symposium on Discrete Algorithms (SODA 2022) (arXiv)

2021

Stochastic Bias-Reduced Gradient Methods

With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin
In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv)

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss

With Yair Carmon, Arun Jambulapati, and Yujia Jin
In Conference on Learning Theory (COLT 2021) (arXiv)

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

With Nima Anari, Moses Charikar, and Kirankumar Shiragur
In Conference on Learning Theory (COLT 2021) (arXiv)

Towards Tight Bounds on the Sample Complexity of Average-reward MDPs

With Yujia Jin
In International Conference on Machine Learning (ICML 2021) (arXiv)

Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances

With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang
In Symposium on Theory of Computing (STOC 2021) (arXiv)

Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers

With Arun Jambulapati
In Symposium on Discrete Algorithms (SODA 2021) (arXiv)

Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration

With Michael B. Cohen and Kevin Tian
In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv)

2020

Acceleration with a Ball Optimization Oracle

With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian
In Conference on Neural Information Processing Systems (NeurIPS 2020)
Oral presentation (arXiv)

Instance Based Approximations to Profile Maximum Likelihood

With Nima Anari, Moses Charikar, and Kirankumar Shiragur
In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv)

Large-Scale Methods for Distributionally Robust Optimization

With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution)
In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv)

High-precision Estimation of Random Walks in Small Space

With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan
In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv)

Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs

With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang
In Symposium on Foundations of Computer Science (FOCS 2020)
Invited to the special issue (arXiv)

Coordinate Methods for Matrix Games

With Yair Carmon, Yujia Jin, and Kevin Tian
In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv)

Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time

With Tarun Kathuria and Yang P. Liu
In Symposium on Foundations of Computer Science (FOCS 2020)
Invited to the special issue (arXiv before merge))

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

With Mengdi Wang, Lin Yang, and Yinyu Ye
In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv)

Efficiently Solving MDPs with Stochastic Mirror Descent

With Yujia Jin
In International Conference on Machine Learning (ICML 2020) (arXiv)

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

With Oliver Hinder and Nimit Sharad Sohoni
In Conference on Learning Theory (COLT 2020) (arXiv)

Solving Tall Dense Linear Programs in Nearly Linear Time

With Jan van den Brand, Yin Tat Lee, and Zhao Song
In Symposium on Theory of Computing (STOC 2020)
Invited to the special issue (arXiv)

Faster energy maximization for faster maximum flow.

With Yang P. Liu,
In Symposium on Theory of Computing (STOC 2020) (arXiv)

Constant Girth Approximation for Directed Graphs in Subquadratic Time

With Shiri Chechik, Yang P. Liu, and Omer Rotem
In Symposium on Theory of Computing (STOC 2020) (arXiv)

Leverage Score Sampling for Faster Accelerated Regression and ERM

With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli
In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv)

Near-optimal Approximate Discrete and Continuous Submodular Function Minimization

With Brian Axelrod and Yang P. Liu
In Symposium on Discrete Algorithms (SODA 2020) (arXiv)

Fast and Space Efficient Spectral Sparsification in Dynamic Streams

With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos
In Symposium on Discrete Algorithms (SODA 2020) (arXiv)

2019

Variance Reduction for Matrix Games

With Yair Carmon, Yujia Jin, and Kevin Tian
In Conference on Neural Information Processing Systems (NeurIPS 2019)
Oral presentation (arXiv)

Complexity of Highly Parallel Non-Smooth Convex Optimization

With Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li
In Conference on Neural Information Processing Systems (NeurIPS 2019)
Spotlight presentation (arXiv)

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

With Yujia Jin
In Conference on Neural Information Processing Systems (NeurIPS 2019)
Spotlight presentation (arXiv)

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

With Arun Jambulapati and Kevin Tian
In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv)

A General Framework for Efficient Symmetric Property Estimation

With Moses Charikar and Kirankumar Shiragur
In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv)

Parallel Reachability in Almost Linear Work and Square Root Depth

With Arun Jambulapati and Yang P. Liu
In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv)

Faster Matroid Intersection

With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong
In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv)

Deterministic Approximation of Random Walks in Small Space

With Jack Murtagh, Omer Reingold, and Salil P. Vadhan
In International Workshop on Randomization and Computation (RANDOM 2019)
Invited to the special issue (arXiv)

A Rank-1 Sketch for Matrix Multiplicative Weights

With Yair Carmon, John C. Duchi, and Kevin Tian
In Conference on Learning Theory (COLT 2019) (arXiv)

Near-optimal method for highly smooth convex optimization

With Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li
In Conference on Learning Theory (COLT 2019) (arXiv)

Efficient profile maximum likelihood for universal symmetric property estimation

With Moses Charikar and Kirankumar Shiragur
In Symposium on Theory of Computing (STOC 2019) (arXiv)

Memory-sample tradeoffs for linear regression with small error

With Vatsal Sharan and Gregory Valiant
In Symposium on Theory of Computing (STOC 2019) (arXiv)

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

With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi
In Symposium on Discrete Algorithms (SODA 2019) (arXiv)

2018

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

With Neha Gupta
In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv)

Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye
In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv)

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

With Kevin Tian
In Symposium on Foundations of Computer Science (FOCS 2018)
Invited to the special issue (arXiv)

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

With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao
In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv)

Efficient Convex Optimization with Membership Oracles

With Yin Tat Lee and Santosh S. Vempala
In Conference on Learning Theory (COLT 2018) (arXiv)

Accelerating Stochastic Gradient Descent for Least Squares Regression

With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli
In Conference on Learning Theory (COLT 2018) (arXiv)

Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners

With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams.
In Symposium on Discrete Algorithms (SODA 2018) (arXiv)

Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes

With Mengdi Wang, Xian Wu, and Yinyu Ye
In Symposium on Discrete Algorithms (SODA 2018) (arXiv)

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

With Arun Jambulapati
In Symposium on Discrete Algorithms (SODA 2018) (arXiv)

Stability of the Lanczos Method for Matrix Function Approximation

With Cameron Musco and Christopher Musco.
In Symposium on Discrete Algorithms (SODA 2018) (arXiv)

Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness

With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff.
In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv)

2017

Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space

With Jack Murtagh, Omer Reingold, and Salil P. Vadhan.
In Symposium on Foundations of Computer Science (FOCS 2017) (arXiv)

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions

With Yair Carmon, John C. Duchi, and Oliver Hinder
In International Conference on Machine Learning (ICML 2017) (arXiv)

Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs

With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, and, Adrian Vladu
In Symposium on Theory of Computing (STOC 2017)
Invited to the special issue (arXiv)

Subquadratic Submodular Function Minimization

With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong
In Symposium on Theory of Computing (STOC 2017) (arXiv)

2016

Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More

With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, and Adrian Vladu
In Symposium on Foundations of Computer Science (FOCS 2016) (arXiv)

Geometric Median in Nearly Linear Time

With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki
In Symposium on Theory of Computing (STOC 2016) (arXiv)

Routing Under Balance

With Alina Ene, Gary L. Miller, and Jakub Pachocki
In Symposium on Theory of Computing (STOC 2016) (arXiv)

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

With Prateek Jain, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli
In Conference on Learning Theory (COLT 2016) (arXiv)

Principal Component Projection Without Principal Component Analysis

With Roy Frostig, Cameron Musco, and Christopher Musco
In International Conference on Machine Learning (ICML 2016) (arXiv)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning

With Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, and Praneeth Netrapalli
In International Conference on Machine Learning (ICML 2016) (arXiv)

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

With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli.
In International Conference on Machine Learning (ICML 2016). (arXiv)

2015

A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization

With Yin Tat Lee and Sam Chiu-wai Wong
In Symposium on Foundations of Computer Science (FOCS 2015)
Machtey Award for Best Student Paper (arXiv)

Efficient Inverse Maintenance and Faster Algorithms for Linear Programming

With Yin Tat Lee
In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv)

Competing with the Empirical Risk Minimizer in a Single Pass

With Roy Frostig, Rong Ge, and Sham Kakade
In Conference on Learning Theory (COLT 2015) (arXiv)

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

With Roy Frostig, Rong Ge, and Sham Kakade
In International Conference on Machine Learning (ICML 2015) (arXiv)

Uniform Sampling for Matrix Approximation

With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng
In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv)

2014

Path-Finding Methods for Linear Programming : Solving Linear Programs in Õ(√rank) Iterations and Faster Algorithms for Maximum Flow

With Yin Tat Lee
In Symposium on Foundations of Computer Science (FOCS 2014)
Best Paper Award and Machtey Award for Best Student Paper (arXiv)

Single Pass Spectral Sparsification in Dynamic Streams

With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco
In Symposium on Foundations of Computer Science (FOCS 2014)
Invited to the special issue (arXiv)

An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations

With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia
In Symposium on Discrete Algorithms (SODA 2014)
Best paper award (arXiv)

2013

Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems

With Yin Tat Lee
In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv)

A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time

With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu
In Symposium on the Theory of Computing (STOC 2013) (arXiv)

Journal and Book Publications

Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time

With Tarun Kathuria and Yang P. Liu
SIAM Journal on Computing (arXiv before merge)

Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space

With Jack Murtagh, Omer Reingold, and Salil Vadhan
SIAM Journal on Computing, 2021 (arXiv)

Deterministic Approximation of Random Walks in Small Space

With Jack Murtagh, Omer Reingold, and Salil P. Vadhan
Theory of Computing, 2021 (arXiv)

Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes

With Mengdi Wang, Xian Wu, and Yinyu Ye
Naval Research Logistics, 2021 (arXiv)

Efficient Convex Optimization with Membership Oracles

With Yin Tat Lee and Santosh S. Vempala
Book chapter in Building Bridges II: Mathematics of László Lovász, 2020 (arXiv)

Lower Bounds for Finding Stationary Points II: First-Order Methods

With Yair Carmon, John C. Duchi, and Oliver Hinder.
Mathematical Programming, 2019 (arXiv)

Lower bounds for finding stationary points I

With Yair Carmon, John C. Duchi, and Oliver Hinder.
Mathematical Programming, 2019 (arXiv)

Accelerated Methods for NonConvex Optimization

With Yair Carmon, John C. Duchi, and Oliver Hinder.
SIAM Journal on Optimization, 2018 (arXiv)

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification

With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli.
Journal of Machine Learning Research, 2017 (arXiv)

Single Pass Spectral Sparsification in Dynamic Streams

With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco.
SIAM Journal on Computing, 2017 (arXiv)