Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. - Yin Tat Lee; Aaron Sidford (FOCS, 2013)

A simple, combinatorial algorithm for solving SDD systems in nearly-linear time. - Jonathan A. Kelner; Lorenzo Orecchia; Aaron Sidford; Zeyuan Allen Zhu (STOC, 2013)

Matching the Universal Barrier Without Paying the Costs : Solving Linear Programs with Õ(sqrt(rank)) Linear System Solves. - Yin Tat Lee; Aaron Sidford (CoRR, 2013)

Following the Path of Least Resistance : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem. - Yin Tat Lee; Aaron Sidford (CoRR, 2013)

Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow. - Yin Tat Lee; Aaron Sidford (FOCS, 2014)

An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. - Jonathan A. Kelner; Yin Tat Lee; Lorenzo Orecchia; Aaron Sidford (SODA, 2014)

Competing with the Empirical Risk Minimizer in a Single Pass. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (COLT, 2015)

Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. - Yin Tat Lee; Aaron Sidford (FOCS, 2015)

A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. - Yin Tat Lee; Aaron Sidford; Sam Chiu-wai Wong (FOCS, 2015)

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. - Roy Frostig; Rong Ge 001; Sham M. Kakade; Aaron Sidford (ICML, 2015)

Uniform Sampling for Matrix Approximation. - Michael B. Cohen; Yin Tat Lee; Cameron Musco; Christopher Musco; Richard Peng; Aaron Sidford (ITCS, 2015)

Polylogarithmic Fully Retroactive Priority Queues via Hierarchical Checkpointing. - Erik D. Demaine; Tim Kaler; Quanquan C. Liu; Aaron Sidford; Adam Yedidia (WADS, 2015)

Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. - Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (CoRR, 2015)

Iterative methods, combinatorial optimization, and linear programming beyond the universal barrier. - Aaron Sidford (2015)

Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (COLT, 2016)

Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. - Michael B. Cohen; Jonathan A. Kelner; John Peebles; Richard Peng; Aaron Sidford; Adrian Vladu (FOCS, 2016)

Principal Component Projection Without Principal Component Analysis. - Roy Frostig; Cameron Musco; Christopher Musco; Aaron Sidford (ICML, 2016)

Faster Eigenvector Computation via Shift-and-Invert Preconditioning. - Dan Garber; Elad Hazan; Chi Jin 001; Sham M. Kakade; Cameron Musco; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. - Rong Ge 001; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (ICML, 2016)

Geometric median in nearly linear time. - Michael B. Cohen; Yin Tat Lee; Gary L. Miller; Jakub Pachocki; Aaron Sidford (STOC, 2016)

Routing under balance. - Alina Ene; Gary L. Miller; Jakub Pachocki; Aaron Sidford (STOC, 2016)

Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. - Prateek Jain 002; Chi Jin 001; Sham M. Kakade; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2016)

Accelerated Methods for Non-Convex Optimization. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (CoRR, 2016)

A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Venkata Krishna Pillutla; Aaron Sidford (FSTTCS, 2017)

"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (ICML, 2017)

Subquadratic submodular function minimization. - Deeparnab Chakrabarty; Yin Tat Lee; Aaron Sidford; Sam Chiu-wai Wong (STOC, 2017)

Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. - Michael B. Cohen; Jonathan A. Kelner; John Peebles; Richard Peng; Anup B. Rao; Aaron Sidford; Adrian Vladu (STOC, 2017)

Accelerating Stochastic Gradient Descent. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (CoRR, 2017)

Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. - Arun Jambulapati; Aaron Sidford (CoRR, 2017)

Arboral satisfaction: Recognition and LP approximation. - Erik D. Demaine; Varun Ganesan; Vladislav Kontsevoi; Qipeng Liu 001; Quanquan C. Liu; Fermi Ma; Ofir Nachum; Aaron Sidford; Erik Waingarten; Daniel Ziegler 002 (Inf. Process. Lett., 2017)

Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (J. Mach. Learn. Res., 2017)

Single Pass Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Yin Tat Lee; Cameron Musco; Christopher Musco; Aaron Sidford (SIAM J. Comput., 2017)

Accelerating Stochastic Gradient Descent for Least Squares Regression. - Prateek Jain 002; Sham M. Kakade; Rahul Kidambi; Praneeth Netrapalli; Aaron Sidford (COLT, 2018)

Efficient Convex Optimization with Membership Oracles. - Yin Tat Lee; Aaron Sidford; Santosh S. Vempala (COLT, 2018)

Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. - Michael B. Cohen; Jonathan A. Kelner; Rasmus Kyng; John Peebles; Richard Peng; Anup B. Rao; Aaron Sidford (FOCS, 2018)

Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. - Aaron Sidford; Kevin Tian (FOCS, 2018)

Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. - Cameron Musco; Praneeth Netrapalli; Aaron Sidford; Shashanka Ubaru; David P. Woodruff (ITCS, 2018)

Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression. - Neha Gupta 002; Aaron Sidford (NeurIPS, 2018)

Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. - Aaron Sidford; Mengdi Wang; Xian Wu 009; Lin Yang 011; Yinyu Ye 001 (NeurIPS, 2018)

Efficient Õ(n/∊) Spectral Sketches for the Laplacian and its Pseudoinverse. - Arun Jambulapati; Aaron Sidford (SODA, 2018)

Stability of the Lanczos Method for Matrix Function Approximation. - Cameron Musco; Christopher Musco; Aaron Sidford (SODA, 2018)

Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. - Jakub Pachocki; Liam Roditty; Aaron Sidford; Roei Tov; Virginia Vassilevska Williams (SODA, 2018)

Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. - Aaron Sidford; Mengdi Wang; Xian Wu 009; Yinyu Ye 001 (SODA, 2018)

Coordinate Methods for Accelerating 𝓁∞ Regression and Faster Approximate Maximum Flow. - Aaron Sidford; Kevin Tian (CoRR, 2018)

Towards Optimal Running Times for Optimal Transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (CoRR, 2018)

Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. - Arun Jambulapati; Kirankumar Shiragur; Aaron Sidford (CoRR, 2018)

Accelerated Methods for NonConvex Optimization. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (SIAM J. Optim., 2018)

Near-optimal method for highly smooth convex optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (COLT, 2019)

A Rank-1 Sketch for Matrix Multiplicative Weights. - Yair Carmon; John C. Duchi; Aaron Sidford; Kevin Tian (COLT, 2019)

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. - Alexander V. Gasnikov; Pavel E. Dvurechensky; Eduard Gorbunov; Evgeniya A. Vorontsova; Daniil Selikhanovych; César A. Uribe; Bo Jiang 007; Haoyue Wang; Shuzhong Zhang; Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; et al. (COLT, 2019)

Faster Matroid Intersection. - Deeparnab Chakrabarty; Yin Tat Lee; Aaron Sidford; Sahil Singla 001; Sam Chiu-wai Wong (FOCS, 2019)

Parallel Reachability in Almost Linear Work and Square Root Depth. - Yang P. Liu; Arun Jambulapati; Aaron Sidford (FOCS, 2019)

Complexity of Highly Parallel Non-Smooth Convex Optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (NeurIPS, 2019)

Variance Reduction for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

A General Framework for Symmetric Property Estimation. - Moses Charikar; Kirankumar Shiragur; Aaron Sidford (NeurIPS, 2019)

A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (NeurIPS, 2019)

Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. - Yujia Jin; Aaron Sidford (NeurIPS, 2019)

Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. - AmirMahdi Ahmadinejad; Arun Jambulapati; Amin Saberi; Aaron Sidford (SODA, 2019)

Efficient profile maximum likelihood for universal symmetric property estimation. - Moses Charikar; Kirankumar Shiragur; Aaron Sidford (STOC, 2019)

Memory-sample tradeoffs for linear regression with small error. - Vatsal Sharan; Aaron Sidford; Gregory Valiant (STOC, 2019)

Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space. - Michael Kapralov; Navid Nouri; Aaron Sidford; Jakab Tardos (CoRR, 2019)

A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. - Arun Jambulapati; Aaron Sidford; Kevin Tian (CoRR, 2019)

Improved Girth Approximation and Roundtrip Spanners. - Shiri Chechik; Yang P. Liu; Omer Rotem; Aaron Sidford (CoRR, 2019)

Solving Linear Programs with Sqrt(rank) Linear System Solves. - Yin Tat Lee; Aaron Sidford (CoRR, 2019)

Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. - Aaron Sidford; Mengdi Wang; Lin Yang 011; Yinyu Ye 001 (AISTATS, 2020)

Leverage Score Sampling for Faster Accelerated Regression and ERM. - Naman Agarwal; Sham M. Kakade; Rahul Kidambi; Yin Tat Lee; Praneeth Netrapalli; Aaron Sidford (ALT, 2020)

Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. - Oliver Hinder; Aaron Sidford; Nimit Sharad Sohoni (COLT, 2020)

High-precision Estimation of Random Walks in Small Space. - AmirMahdi Ahmadinejad; Jonathan A. Kelner; Jack Murtagh; John Peebles; Aaron Sidford; Salil P. Vadhan (FOCS, 2020)

Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. - Jan van den Brand; Yin Tat Lee; Danupon Nanongkai; Richard Peng; Thatchaphol Saranurak; Aaron Sidford; Zhao Song 002; Di Wang 005 (FOCS, 2020)

Coordinate Methods for Matrix Games. - Yair Carmon; Yujia Jin; Aaron Sidford; Kevin Tian (FOCS, 2020)

Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time. - Tarun Kathuria; Yang P. Liu; Aaron Sidford (FOCS, 2020)

Efficiently Solving MDPs with Stochastic Mirror Descent. - Yujia Jin; Aaron Sidford (ICML, 2020)

Instance Based Approximations to Profile Maximum Likelihood. - Nima Anari; Moses Charikar; Kirankumar Shiragur; Aaron Sidford (NeurIPS, 2020)

Acceleration with a Ball Optimization Oracle. - Yair Carmon; Arun Jambulapati; Qijia Jiang; Yujia Jin; Yin Tat Lee; Aaron Sidford; Kevin Tian (NeurIPS, 2020)

Large-Scale Methods for Distributionally Robust Optimization. - Daniel Levy; Yair Carmon; John C. Duchi; Aaron Sidford (NeurIPS, 2020)

Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. - Brian Axelrod; Yang P. Liu; Aaron Sidford (SODA, 2020)

Fast and Space Efficient Spectral Sparsification in Dynamic Streams. - Michael Kapralov; Aida Mousavifar; Cameron Musco; Christopher Musco; Navid Nouri; Aaron Sidford; Jakab Tardos (SODA, 2020)

Solving tall dense linear programs in nearly linear time. - Jan van den Brand; Yin Tat Lee; Aaron Sidford; Zhao Song 002 (STOC, 2020)

Constant girth approximation for directed graphs in subquadratic time. - Shiri Chechik; Yang P. Liu; Omer Rotem; Aaron Sidford (STOC, 2020)

Faster energy maximization for faster maximum flow. - Yang P. Liu; Aaron Sidford (STOC, 2020)

Faster Divergence Maximization for Faster Maximum Flow. - Yang P. Liu; Aaron Sidford (CoRR, 2020)

Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. - Jerry Li 001; Aaron Sidford; Kevin Tian; Huishuai Zhang (CoRR, 2020)

Semi-Streaming Bipartite Matching in Fewer Passes and Less Space. - Yujia Jin; Aaron Sidford; Kevin Tian (CoRR, 2020)

Lower bounds for finding stationary points I. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (Math. Program., 2020)

The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood. - Nima Anari; Moses Charikar; Kirankumar Shiragur; Aaron Sidford (COLT, 2021)

Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (COLT, 2021)

Towards Tight Bounds on the Sample Complexity of Average-reward MDPs. - Yujia Jin; Aaron Sidford (ICML, 2021)

Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. - Michael B. Cohen; Aaron Sidford; Kevin Tian (ITCS, 2021)

Stochastic Bias-Reduced Gradient Methods. - Hilal Asi; Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2021)

Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances. - Jan van den Brand; Yin Tat Lee; Yang P. Liu; Thatchaphol Saranurak; Aaron Sidford; Zhao Song 002; Di Wang 005 (STOC, 2021)

Improved Iteration Complexities for Overconstrained p-Norm Regression. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (CoRR, 2021)

Lower bounds for finding stationary points II: first-order methods. - Yair Carmon; John C. Duchi; Oliver Hinder; Aaron Sidford (Math. Program., 2021)

Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. - Jack Murtagh; Omer Reingold; Aaron Sidford; Salil P. Vadhan (SIAM J. Comput., 2021)

Deterministic Approximation of Random Walks in Small Space. - Jack Murtagh; Omer Reingold; Aaron Sidford; Salil P. Vadhan (Adv. Math. Commun., 2021)

Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. - Yujia Jin; Aaron Sidford; Kevin Tian (COLT, 2022)

Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. - Jonathan A. Kelner; Annie Marsden; Vatsal Sharan; Aaron Sidford; Gregory Valiant; Honglin Yuan (COLT, 2022)

Improved Lower Bounds for Submodular Function Minimization. - Deeparnab Chakrabarty; Andrei Graur; Haotian Jiang; Aaron Sidford (FOCS, 2022)

Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary. - Aaron Bernstein; Jan van den Brand; Maximilian Probst Gutenberg; Danupon Nanongkai; Thatchaphol Saranurak; Aaron Sidford; He Sun 001 (ICALP, 2022)

Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. - Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (ICALP, 2022)

RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (ICML, 2022)

Optimal and Adaptive Monteiro-Svaiter Acceleration. - Yair Carmon; Danielle Hausler; Arun Jambulapati; Yujia Jin; Aaron Sidford (NeurIPS, 2022)

On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. - Moses Charikar; Zhihao Jiang; Kirankumar Shiragur; Aaron Sidford (NeurIPS, 2022)

Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space. - Sepehr Assadi; Arun Jambulapati; Yujia Jin; Aaron Sidford; Kevin Tian (SODA, 2022)

Computing Lewis Weights to High Precision. - Maryam Fazel; Yin Tat Lee; Swati Padmanabhan; Aaron Sidford (SODA, 2022)

Algorithmic trade-offs for girth approximation in undirected graphs. - Avi Kadria; Liam Roditty; Aaron Sidford; Virginia Vassilevska Williams; Uri Zwick (SODA, 2022)

Faster maxflow via improved dynamic spectral vertex sparsifiers. - Jan van den Brand; Yu Gao 001; Arun Jambulapati; Yin Tat Lee; Yang P. Liu; Richard Peng; Aaron Sidford (STOC, 2022)

Improved iteration complexities for overconstrained p-norm regression. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (STOC, 2022)

Moments, Random Walks, and Limits for Spectrum Approximation. - Yujia Jin; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (COLT, 2023)

Semi-Random Sparse Recovery in Nearly-Linear Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (COLT, 2023)

Singular Value Approximation and Sparsifying Random Walks on Directed Graphs. - AmirMahdi Ahmadinejad; John Peebles; Edward Pyne; Aaron Sidford; Salil P. Vadhan (FOCS, 2023)

A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. - Jan van den Brand; Li Chen 028; Richard Peng; Rasmus Kyng; Yang P. Liu; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (FOCS, 2023)

ReSQueing Parallel and Private Stochastic Convex Optimization. - Yair Carmon; Arun Jambulapati; Yujia Jin; Yin Tat Lee; Daogao Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Sparse Submodular Function Minimization. - Andrei Graur; Haotian Jiang; Aaron Sidford (FOCS, 2023)

Sparsifying Sums of Norms. - Arun Jambulapati; James R. Lee; Yang P. Liu; Aaron Sidford (FOCS, 2023)

Matrix Completion in Almost-Verification Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. - Adam Bouland; Yosheb M. Getachew; Yujia Jin; Aaron Sidford; Kevin Tian (ICML, 2023)

Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). - Annie Marsden; Vatsal Sharan; Aaron Sidford; Gregory Valiant (IJCAI, 2023)

The Complexity of Infinite-Horizon General-Sum Stochastic Games. - Yujia Jin; Vidya Muthukumar; Aaron Sidford (ITCS, 2023)

Parallel Submodular Function Minimization. - Deeparnab Chakrabarty; Andrei Graur; Haotian Jiang; Aaron Sidford (NeurIPS, 2023)

Towards Optimal Effective Resistance Estimation. - Rajat Vadiraj Dwaraknath; Ishani Karmarkar; Aaron Sidford (NeurIPS, 2023)

Structured Semidefinite Programming for Recovering Structured Preconditioners. - Arun Jambulapati; Jerry Li 001; Christopher Musco; Kirankumar Shiragur; Aaron Sidford; Kevin Tian (NeurIPS, 2023)

Quantum speedups for stochastic optimization. - Aaron Sidford; Chenyi Zhang 003 (NeurIPS, 2023)

Improved girth approximation in weighted undirected graphs. - Avi Kadria; Liam Roditty; Aaron Sidford; Virginia Vassilevska Williams; Uri Zwick (SODA, 2023)

Dynamic Maxflow via Dynamic Interior Point Methods. - Jan van den Brand; Yang P. Liu; Aaron Sidford (STOC, 2023)

Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. - Arun Jambulapati; Yang P. Liu; Aaron Sidford (STOC, 2023)

Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification. - AmirMahdi Ahmadinejad; John Peebles; Edward Pyne; Aaron Sidford; Salil P. Vadhan (CoRR, 2023)

Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. - Arun Jambulapati; Aaron Sidford; Kevin Tian (COLT, 2024)

Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract). - Yujia Jin; Ishani Karmarkar; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (COLT, 2024)

Truncated Variance Reduced Value Iteration. - Yujia Jin; Ishani Karmarkar; Aaron Sidford; Jiayi Wang (NeurIPS, 2024)

Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (NeurIPS, 2024)

Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. - Jan van den Brand; Li Chen 028; Rasmus Kyng; Yang P. Liu; Richard Peng; Maximilian Probst Gutenberg; Sushant Sachdeva; Aaron Sidford (SODA, 2024)

A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. - Yair Carmon; Arun Jambulapati; Yujia Jin; Aaron Sidford (SODA, 2024)

Near-Optimal Dynamic Rounding of Fractional Matchings in Bipartite Graphs. - Sayan Bhattacharya; Peter Kiss; Aaron Sidford; David Wajc (STOC, 2024)

Sparsifying Generalized Linear Models. - Arun Jambulapati; James R. Lee; Yang P. Liu; Aaron Sidford (STOC, 2024)

On computing approximate Lewis weights. - Simon Apers; Sander Gribling; Aaron Sidford (CoRR, 2024)

Faster Spectral Density Estimation and Sparsification in the Nuclear Norm. - Yujia Jin; Ishani Karmarkar; Christopher Musco; Aaron Sidford; Apoorv Vikram Singh (CoRR, 2024)

Convex optimization with p-norm oracles. - Deeksha Adil; Brian Bullins; Arun Jambulapati; Aaron Sidford (CoRR, 2024)

Towards optimal running timesfor optimal transport. - Jose H. Blanchet; Arun Jambulapati; Carson Kent; Aaron Sidford (Oper. Res. Lett., 2024)

Unit Capacity Maxflow in Almost $m^{4/3}$ Time. - Tarun Kathuria; Yang P. Liu; Aaron Sidford (SIAM J. Comput., 2024)

Efficient Convex Optimization Requires Superlinear Memory. - Annie Marsden; Vatsal Sharan; Aaron Sidford; Gregory Valiant (J. ACM, 2024)

Extracting Dual Solutions via Primal Optimizers. - Yair Carmon; Arun Jambulapati; Liam O'Carroll; Aaron Sidford (ITCS, 2025)

Entropy Regularization and Faster Decremental Matching in General Graphs. - Jiale Chen 003; Aaron Sidford; Ta-Wei Tu (SODA, 2025)

Matching Composition and Efficient Weight Reduction in Dynamic Matching. - Aaron Bernstein; Jiale Chen 003; Aditi Dudeja; Zachary Langley; Aaron Sidford; Ta-Wei Tu (SODA, 2025)

Eulerian Graph Sparsification by Effective Resistance Decomposition. - Arun Jambulapati; Sushant Sachdeva; Aaron Sidford; Kevin Tian; Yibin Zhao 003 (SODA, 2025)

Accelerated Approximate Optimization of Multi-commodity Flows on Directed Graphs. - Li Chen 028; Andrei Graur; Aaron Sidford (STOC, 2025)

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure. - Michal Derezinski; Aaron Sidford (CoRR, 2025)

Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. - Arun Jambulapati; Aaron Sidford (ACM Trans. Algorithms, 2025)