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ORCIDAaron Sidford
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- affiliation: Stanford University, CA, USA
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2020 – today
- 2025
[c115]Yair Carmon, Arun Jambulapati, Liam O'Carroll, Aaron Sidford
:
Extracting Dual Solutions via Primal Optimizers. ITCS 2025: 29:1-29:24- [c114]Arun Jambulapati, Sushant Sachdeva, Aaron Sidford, Kevin Tian, Yibin Zhao:
Eulerian Graph Sparsification by Effective Resistance Decomposition. SODA 2025: 1607-1650 - [c113]Aaron Bernstein, Jiale Chen, Aditi Dudeja, Zachary Langley, Aaron Sidford, Ta-Wei Tu:
Matching Composition and Efficient Weight Reduction in Dynamic Matching. SODA 2025: 2991-3028 - [c112]Jiale Chen, Aaron Sidford, Ta-Wei Tu:
Entropy Regularization and Faster Decremental Matching in General Graphs. SODA 2025: 3069-3115 - [i117]Li Chen, Andrei Graur, Aaron Sidford:
Accelerated Approximate Optimization of Multi-Commodity Flows on Directed Graphs. CoRR abs/2503.24373 (2025) - 2024
- [j11]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. J. ACM 71(6): 41:1-41:37 (2024) - [j10]Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards optimal running timesfor optimal transport. Oper. Res. Lett. 52: 107054 (2024) - [j9]Tarun Kathuria, Yang P. Liu, Aaron Sidford:
Unit Capacity Maxflow in Almost $m^{4/3}$ Time. SIAM J. Comput. 53(6): S20-175 (2024) - [c111]Arun Jambulapati, Aaron Sidford, Kevin Tian:
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. COLT 2024: 2608-2643 - [c110]Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract). COLT 2024: 2722 - [c109]Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang:
Truncated Variance Reduced Value Iteration. NeurIPS 2024 - [c108]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. NeurIPS 2024 - [c107]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. SODA 2024: 2980-2998 - [c106]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. SODA 2024: 3685-3723 - [c105]Sayan Bhattacharya, Peter Kiss, Aaron Sidford, David Wajc:
Near-Optimal Dynamic Rounding of Fractional Matchings in Bipartite Graphs. STOC 2024: 59-70 - [c104]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Generalized Linear Models. STOC 2024: 1665-1675 - [i116]Simon Apers, Sander Gribling, Aaron Sidford:
On computing approximate Lewis weights. CoRR abs/2404.02881 (2024) - [i115]Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang:
Truncated Variance Reduced Value Iteration. CoRR abs/2405.12952 (2024) - [i114]Arun Jambulapati, Aaron Sidford, Kevin Tian:
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization. CoRR abs/2406.07373 (2024) - [i113]Yujia Jin, Ishani Karmarkar, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm. CoRR abs/2406.07521 (2024) - [i112]Arun Jambulapati, Sushant Sachdeva, Aaron Sidford, Kevin Tian, Yibin Zhao:
Eulerian Graph Sparsification by Effective Resistance Decomposition. CoRR abs/2408.10172 (2024) - [i111]Aaron Bernstein, Jiale Chen, Aditi Dudeja, Zachary Langley, Aaron Sidford, Ta-Wei Tu:
Matching Composition and Efficient Weight Reduction in Dynamic Matching. CoRR abs/2410.18936 (2024) - [i110]Deeksha Adil, Brian Bullins, Arun Jambulapati, Aaron Sidford:
Convex optimization with p-norm oracles. CoRR abs/2410.24158 (2024) - [i109]Yair Carmon, Arun Jambulapati, Liam O'Carroll, Aaron Sidford:
Extracting Dual Solutions via Primal Optimizers. CoRR abs/2412.02949 (2024) - 2023
- [c103]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Sparse Recovery in Nearly-Linear Time. COLT 2023: 2352-2398 - [c102]Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Moments, Random Walks, and Limits for Spectrum Approximation. COLT 2023: 5373-5394 - [c101]Jan van den Brand, Li Chen, Richard Peng, Rasmus Kyng, Yang P. Liu, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. FOCS 2023: 503-514 - [c100]AmirMahdi Ahmadinejad, John Peebles, Edward Pyne, Aaron Sidford, Salil P. Vadhan:
Singular Value Approximation and Sparsifying Random Walks on Directed Graphs. FOCS 2023: 846-854 - [c99]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Sums of Norms. FOCS 2023: 1953-1962 - [c98]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. FOCS 2023: 2031-2058 - [c97]Andrei Graur, Haotian Jiang, Aaron Sidford:
Sparse Submodular Function Minimization. FOCS 2023: 2071-2080 - [c96]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Matrix Completion in Almost-Verification Time. FOCS 2023: 2102-2128 - [c95]Adam Bouland, Yosheb M. Getachew, Yujia Jin, Aaron Sidford, Kevin Tian:
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. ICML 2023: 2932-2952 - [c94]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract). IJCAI 2023: 6468-6473 - [c93]Yujia Jin, Vidya Muthukumar, Aaron Sidford:
The Complexity of Infinite-Horizon General-Sum Stochastic Games. ITCS 2023: 76:1-76:20 - [c92]Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford:
Parallel Submodular Function Minimization. NeurIPS 2023 - [c91]Rajat Vadiraj Dwaraknath, Ishani Karmarkar, Aaron Sidford:
Towards Optimal Effective Resistance Estimation. NeurIPS 2023 - [c90]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. NeurIPS 2023 - [c89]Aaron Sidford, Chenyi Zhang:
Quantum speedups for stochastic optimization. NeurIPS 2023 - [c88]Avi Kadria, Liam Roditty, Aaron Sidford, Virginia Vassilevska Williams, Uri Zwick:
Improved girth approximation in weighted undirected graphs. SODA 2023: 2242-2255 - [c87]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. STOC 2023: 196-206 - [c86]Jan van den Brand, Yang P. Liu, Aaron Sidford:
Dynamic Maxflow via Dynamic Interior Point Methods. STOC 2023: 1215-1228 - [i108]Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian:
ReSQueing Parallel and Private Stochastic Convex Optimization. CoRR abs/2301.00457 (2023) - [i107]Adam Bouland, Yosheb Getachew, Yujia Jin, Aaron Sidford, Kevin Tian:
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling. CoRR abs/2301.03763 (2023) - [i106]AmirMahdi Ahmadinejad, John Peebles, Edward Pyne, Aaron Sidford, Salil P. Vadhan:
Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification. CoRR abs/2301.13541 (2023) - [i105]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying Sums of Norms. CoRR abs/2305.09049 (2023) - [i104]Sayan Bhattacharya, Peter Kiss, Aaron Sidford, David Wajc:
Near-Optimal Dynamic Rounding of Fractional Matchings in Bipartite Graphs. CoRR abs/2306.11828 (2023) - [i103]Rajat Vadiraj Dwaraknath, Ishani Karmarkar, Aaron Sidford:
Towards Optimal Effective Resistance Estimation. CoRR abs/2306.14820 (2023) - [i102]Yujia Jin, Christopher Musco, Aaron Sidford, Apoorv Vikram Singh:
Moments, Random Walks, and Limits for Spectrum Approximation. CoRR abs/2307.00474 (2023) - [i101]Aaron Sidford, Chenyi Zhang:
Quantum speedups for stochastic optimization. CoRR abs/2308.01582 (2023) - [i100]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Matrix Completion in Almost-Verification Time. CoRR abs/2308.03661 (2023) - [i99]Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford:
Parallel Submodular Function Minimization. CoRR abs/2309.04643 (2023) - [i98]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
A Deterministic Almost-Linear Time Algorithm for Minimum-Cost Flow. CoRR abs/2309.16629 (2023) - [i97]Andrei Graur, Haotian Jiang, Aaron Sidford:
Sparse Submodular Function Minimization. CoRR abs/2309.16632 (2023) - [i96]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. CoRR abs/2310.18265 (2023) - [i95]Jan van den Brand, Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Aaron Sidford:
Incremental Approximate Maximum Flow on Undirected Graphs in Subpolynomial Update Time. CoRR abs/2311.03174 (2023) - [i94]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions. CoRR abs/2311.10886 (2023) - [i93]Arun Jambulapati, James R. Lee, Yang P. Liu, Aaron Sidford:
Sparsifying generalized linear models. CoRR abs/2311.18145 (2023) - [i92]Jiale Chen, Aaron Sidford, Ta-Wei Tu:
Entropy Regularization and Faster Decremental Matching in General Graphs. CoRR abs/2312.09077 (2023) - 2022
- [c85]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. COLT 2022: 2390-2430 - [c84]Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan:
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. COLT 2022: 2431-2540 - [c83]Yujia Jin, Aaron Sidford, Kevin Tian:
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. COLT 2022: 4362-4415 - [c82]Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford:
Improved Lower Bounds for Submodular Function Minimization. FOCS 2022: 245-254 - [c81]Aaron Bernstein, Jan van den Brand, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford, He Sun:
Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary. ICALP 2022: 20:1-20:20 - [c80]Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. ICALP 2022: 77:1-77:20 - [c79]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. ICML 2022: 2658-2685 - [c78]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. NeurIPS 2022 - [c77]Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford:
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. NeurIPS 2022 - [c76]Sepehr Assadi, Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space. SODA 2022: 627-669 - [c75]Avi Kadria, Liam Roditty, Aaron Sidford, Virginia Vassilevska Williams, Uri Zwick:
Algorithmic trade-offs for girth approximation in undirected graphs. SODA 2022: 1471-1492 - [c74]Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford:
Computing Lewis Weights to High Precision. SODA 2022: 2723-2742 - [c73]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Improved iteration complexities for overconstrained p-norm regression. STOC 2022: 529-542 - [c72]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster maxflow via improved dynamic spectral vertex sparsifiers. STOC 2022: 543-556 - [i91]Yujia Jin, Aaron Sidford, Kevin Tian:
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. CoRR abs/2202.04640 (2022) - [i90]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Sparse Recovery in Nearly-Linear Time. CoRR abs/2203.04002 (2022) - [i89]Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. CoRR abs/2203.15260 (2022) - [i88]Yujia Jin, Vidya Muthukumar, Aaron Sidford:
The Complexity of Infinite-Horizon General-Sum Stochastic Games. CoRR abs/2204.04186 (2022) - [i87]Arun Jambulapati, Yujia Jin, Aaron Sidford, Kevin Tian:
Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching. CoRR abs/2204.12721 (2022) - [i86]Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Optimal and Adaptive Monteiro-Svaiter Acceleration. CoRR abs/2205.15371 (2022) - [i85]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization. CoRR abs/2206.08627 (2022) - [i84]Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford:
Improved Lower Bounds for Submodular Function Minimization. CoRR abs/2207.04342 (2022) - [i83]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification. CoRR abs/2209.10539 (2022) - [i82]Moses Charikar, Zhihao Jiang, Kirankumar Shiragur, Aaron Sidford:
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood. CoRR abs/2210.06728 (2022) - [i81]Jan van den Brand, Yang P. Liu, Aaron Sidford:
Dynamic Maxflow via Dynamic Interior Point Methods. CoRR abs/2212.06315 (2022) - 2021
- [j8]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Lower bounds for finding stationary points II: first-order methods. Math. Program. 185(1-2): 315-355 (2021) - [j7]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. SIAM J. Comput. 50(6): 1892-1922 (2021) - [j6]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Deterministic Approximation of Random Walks in Small Space. Adv. Math. Commun. 17: 1-35 (2021) - [c71]Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood. COLT 2021: 93-158 - [c70]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. COLT 2021: 866-882 - [c69]Yujia Jin, Aaron Sidford:
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs. ICML 2021: 5055-5064 - [c68]Michael B. Cohen, Aaron Sidford, Kevin Tian:
Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. ITCS 2021: 62:1-62:18 - [c67]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. NeurIPS 2021: 10810-10822 - [c66]Arun Jambulapati, Aaron Sidford:
Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. SODA 2021: 540-559 - [c65]Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances. STOC 2021: 859-869 - [i80]Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Minimum Cost Flows, MDPs, and 𝓁1-Regression in Nearly Linear Time for Dense Instances. CoRR abs/2101.05719 (2021) - [i79]Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss. CoRR abs/2105.01778 (2021) - [i78]Yujia Jin, Aaron Sidford:
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs. CoRR abs/2106.07046 (2021) - [i77]Hilal Asi, Yair Carmon, Arun Jambulapati, Yujia Jin, Aaron Sidford:
Stochastic Bias-Reduced Gradient Methods. CoRR abs/2106.09481 (2021) - [i76]Maryam Fazel, Yin Tat Lee, Swati Padmanabhan, Aaron Sidford:
Computing Lewis Weights to High Precision. CoRR abs/2110.15563 (2021) - [i75]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Improved Iteration Complexities for Overconstrained p-Norm Regression. CoRR abs/2111.01848 (2021) - [i74]Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan:
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. CoRR abs/2111.03137 (2021) - [i73]Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, Richard Peng, Aaron Sidford:
Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers. CoRR abs/2112.00722 (2021) - 2020
- [j5]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Lower bounds for finding stationary points I. Math. Program. 184(1): 71-120 (2020) - [c64]Aaron Sidford, Mengdi Wang, Lin Yang, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. AISTATS 2020: 2992-3002 - [c63]Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. ALT 2020: 22-47 - [c62]Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni:
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. COLT 2020: 1894-1938 - [c61]Tarun Kathuria, Yang P. Liu, Aaron Sidford:
Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time. FOCS 2020: 119-130 - [c60]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Coordinate Methods for Matrix Games. FOCS 2020: 283-293 - [c59]Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. FOCS 2020: 919-930 - [c58]AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, Aaron Sidford, Salil P. Vadhan:
High-precision Estimation of Random Walks in Small Space. FOCS 2020: 1295-1306 - [c57]Yujia Jin, Aaron Sidford:
Efficiently Solving MDPs with Stochastic Mirror Descent. ICML 2020: 4890-4900 - [c56]Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
Instance Based Approximations to Profile Maximum Likelihood. NeurIPS 2020 - [c55]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. NeurIPS 2020 - [c54]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. NeurIPS 2020 - [c53]Brian Axelrod, Yang P. Liu, Aaron Sidford:
Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. SODA 2020: 837-853 - [c52]Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, Aaron Sidford, Jakab Tardos:
Fast and Space Efficient Spectral Sparsification in Dynamic Streams. SODA 2020: 1814-1833 - [c51]Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song:
Solving tall dense linear programs in nearly linear time. STOC 2020: 775-788 - [c50]Yang P. Liu, Aaron Sidford:
Faster energy maximization for faster maximum flow. STOC 2020: 803-814 - [c49]Shiri Chechik, Yang P. Liu, Omer Rotem, Aaron Sidford:
Constant girth approximation for directed graphs in subquadratic time. STOC 2020: 1010-1023 - [i72]Jan van den Brand, Yin Tat Lee, Aaron Sidford, Zhao Song:
Solving Tall Dense Linear Programs in Nearly Linear Time. CoRR abs/2002.02304 (2020) - [i71]Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
A General Framework for Symmetric Property Estimation. CoRR abs/2003.00844 (2020) - [i70]Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian:
Acceleration with a Ball Optimization Oracle. CoRR abs/2003.08078 (2020) - [i69]Yang P. Liu, Aaron Sidford:
Faster Divergence Maximization for Faster Maximum Flow. CoRR abs/2003.08929 (2020) - [i68]Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood. CoRR abs/2004.02425 (2020) - [i67]Aaron Bernstein, Jan van den Brand, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford, He Sun:
Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary. CoRR abs/2004.08432 (2020) - [i66]Jerry Li, Aaron Sidford, Kevin Tian, Huishuai Zhang:
Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. CoRR abs/2008.01722 (2020) - [i65]Yujia Jin, Aaron Sidford:
Efficiently Solving MDPs with Stochastic Mirror Descent. CoRR abs/2008.12776 (2020) - [i64]Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang:
Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. CoRR abs/2009.01802 (2020) - [i63]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Coordinate Methods for Matrix Games. CoRR abs/2009.08447 (2020) - [i62]Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford:
Large-Scale Methods for Distributionally Robust Optimization. CoRR abs/2010.05893 (2020) - [i61]Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
Instance Based Approximations to Profile Maximum Likelihood. CoRR abs/2011.02761 (2020) - [i60]Yujia Jin, Aaron Sidford, Kevin Tian:
Semi-Streaming Bipartite Matching in Fewer Passes and Less Space. CoRR abs/2011.03495 (2020) - [i59]Michael B. Cohen, Aaron Sidford, Kevin Tian:
Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. CoRR abs/2011.06572 (2020) - [i58]Arun Jambulapati, Aaron Sidford:
Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. CoRR abs/2011.08806 (2020)
2010 – 2019
- 2019
- [c48]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Deterministic Approximation of Random Walks in Small Space. APPROX-RANDOM 2019: 42:1-42:22 - [c47]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Near-optimal method for highly smooth convex optimization. COLT 2019: 492-507 - [c46]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. COLT 2019: 589-623 - [c45]Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. COLT 2019: 1392-1393 - [c44]Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong:
Faster Matroid Intersection. FOCS 2019: 1146-1168 - [c43]Yang P. Liu, Arun Jambulapati, Aaron Sidford:
Parallel Reachability in Almost Linear Work and Square Root Depth. FOCS 2019: 1664-1686 - [c42]Yujia Jin, Aaron Sidford:
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. NeurIPS 2019: 3863-3873 - [c41]Arun Jambulapati, Aaron Sidford, Kevin Tian:
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. NeurIPS 2019: 11355-11366 - [c40]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Variance Reduction for Matrix Games. NeurIPS 2019: 11377-11388 - [c39]Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
A General Framework for Symmetric Property Estimation. NeurIPS 2019: 12426-12436 - [c38]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Complexity of Highly Parallel Non-Smooth Convex Optimization. NeurIPS 2019: 13900-13909 - [c37]AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford:
Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. SODA 2019: 1387-1404 - [c36]Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
Efficient profile maximum likelihood for universal symmetric property estimation. STOC 2019: 780-791 - [c35]Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Memory-sample tradeoffs for linear regression with small error. STOC 2019: 890-901 - [i57]Yair Carmon, John C. Duchi, Aaron Sidford, Kevin Tian:
A Rank-1 Sketch for Matrix Multiplicative Weights. CoRR abs/1903.02675 (2019) - [i56]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Deterministic Approximation of Random Walks in Small Space. CoRR abs/1903.06361 (2019) - [i55]Michael Kapralov, Navid Nouri, Aaron Sidford, Jakab Tardos:
Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space. CoRR abs/1903.12150 (2019) - [i54]Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Memory-Sample Tradeoffs for Linear Regression with Small Error. CoRR abs/1904.08544 (2019) - [i53]Moses Charikar, Kirankumar Shiragur, Aaron Sidford:
Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation. CoRR abs/1905.08448 (2019) - [i52]Arun Jambulapati, Yang P. Liu, Aaron Sidford:
Parallel Reachability in Almost Linear Work and Square Root Depth. CoRR abs/1905.08841 (2019) - [i51]Arun Jambulapati, Aaron Sidford, Kevin Tian:
A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. CoRR abs/1906.00618 (2019) - [i50]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Complexity of Highly Parallel Non-Smooth Convex Optimization. CoRR abs/1906.10655 (2019) - [i49]Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni:
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. CoRR abs/1906.11985 (2019) - [i48]Yair Carmon, Yujia Jin, Aaron Sidford, Kevin Tian:
Variance Reduction for Matrix Games. CoRR abs/1907.02056 (2019) - [i47]Shiri Chechik, Yang P. Liu, Omer Rotem, Aaron Sidford:
Improved Girth Approximation and Roundtrip Spanners. CoRR abs/1907.10779 (2019) - [i46]Aaron Sidford, Mengdi Wang, Lin F. Yang, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. CoRR abs/1908.11071 (2019) - [i45]Brian Axelrod, Yang P. Liu, Aaron Sidford:
Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. CoRR abs/1909.00171 (2019) - [i44]Yujia Jin, Aaron Sidford:
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. CoRR abs/1910.06517 (2019) - [i43]Yin Tat Lee, Aaron Sidford:
Solving Linear Programs with Sqrt(rank) Linear System Solves. CoRR abs/1910.08033 (2019) - [i42]Yang P. Liu, Aaron Sidford:
Faster Energy Maximization for Faster Maximum Flow. CoRR abs/1910.14276 (2019) - [i41]Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sahil Singla, Sam Chiu-wai Wong:
Faster Matroid Intersection. CoRR abs/1911.10765 (2019) - [i40]AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, Aaron Sidford, Salil P. Vadhan:
High-precision Estimation of Random Walks in Small Space. CoRR abs/1912.04524 (2019) - 2018
- [j4]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for NonConvex Optimization. SIAM J. Optim. 28(2): 1751-1772 (2018) - [c34]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent for Least Squares Regression. COLT 2018: 545-604 - [c33]Yin Tat Lee, Aaron Sidford, Santosh S. Vempala:
Efficient Convex Optimization with Membership Oracles. COLT 2018: 1292-1294 - [c32]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. FOCS 2018: 898-909 - [c31]Aaron Sidford, Kevin Tian:
Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. FOCS 2018: 922-933 - [c30]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ITCS 2018: 8:1-8:21 - [c29]Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, Yinyu Ye:
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. NeurIPS 2018: 5192-5202 - [c28]Neha Gupta, Aaron Sidford:
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression. NeurIPS 2018: 5274-5283 - [c27]Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye:
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. SODA 2018: 770-787 - [c26]Jakub Pachocki, Liam Roditty, Aaron Sidford, Roei Tov, Virginia Vassilevska Williams:
Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. SODA 2018: 1374-1392 - [c25]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. SODA 2018: 1605-1624 - [c24]Arun Jambulapati, Aaron Sidford:
Efficient Õ(n/∊) Spectral Sketches for the Laplacian and its Pseudoinverse. SODA 2018: 2487-2503 - [i39]Aaron Sidford, Kevin Tian:
Coordinate Methods for Accelerating 𝓁∞ Regression and Faster Approximate Maximum Flow. CoRR abs/1808.01278 (2018) - [i38]AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford:
Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. CoRR abs/1810.02348 (2018) - [i37]Jose H. Blanchet, Arun Jambulapati, Carson Kent, Aaron Sidford:
Towards Optimal Running Times for Optimal Transport. CoRR abs/1810.07717 (2018) - [i36]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. CoRR abs/1811.10722 (2018) - [i35]Neha Gupta, Aaron Sidford:
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression. CoRR abs/1811.10866 (2018) - [i34]Arun Jambulapati, Kirankumar Shiragur, Aaron Sidford:
Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. CoRR abs/1812.06295 (2018) - 2017
- [j3]Erik D. Demaine, Varun Ganesan, Vladislav Kontsevoi, Qipeng Liu, Quanquan C. Liu, Fermi Ma, Ofir Nachum, Aaron Sidford, Erik Waingarten, Daniel Ziegler:
Arboral satisfaction: Recognition and LP approximation. Inf. Process. Lett. 127: 1-5 (2017) - [j2]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. J. Mach. Learn. Res. 18: 223:1-223:42 (2017) - [j1]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. SIAM J. Comput. 46(1): 456-477 (2017) - [c23]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. FOCS 2017: 801-812 - [c22]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). FSTTCS 2017: 2:1-2:10 - [c21]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
"Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. ICML 2017: 654-663 - [c20]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. STOC 2017: 410-419 - [c19]Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong:
Subquadratic submodular function minimization. STOC 2017: 1220-1231 - [i33]Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff:
Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR abs/1704.04163 (2017) - [i32]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Accelerating Stochastic Gradient Descent. CoRR abs/1704.08227 (2017) - [i31]Yin Tat Lee, Aaron Sidford, Santosh S. Vempala:
Efficient Convex Optimization with Membership Oracles. CoRR abs/1706.07357 (2017) - [i30]Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan:
Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. CoRR abs/1708.04634 (2017) - [i29]Cameron Musco, Christopher Musco, Aaron Sidford:
Stability of the Lanczos Method for Matrix Function Approximation. CoRR abs/1708.07788 (2017) - [i28]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford:
A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). CoRR abs/1710.09430 (2017) - [i27]Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye:
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. CoRR abs/1710.09988 (2017) - [i26]Arun Jambulapati, Aaron Sidford:
Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. CoRR abs/1711.00571 (2017) - [i25]Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford:
Leverage Score Sampling for Faster Accelerated Regression and ERM. CoRR abs/1711.08426 (2017) - 2016
- [c18]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. COLT 2016: 1147-1164 - [c17]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, Adrian Vladu:
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. FOCS 2016: 583-592 - [c16]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. ICML 2016: 2349-2357 - [c15]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. ICML 2016: 2626-2634 - [c14]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. ICML 2016: 2741-2750 - [c13]Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, Aaron Sidford:
Geometric median in nearly linear time. STOC 2016: 9-21 - [c12]Alina Ene, Gary L. Miller, Jakub Pachocki, Aaron Sidford:
Routing under balance. STOC 2016: 598-611 - [i24]Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford:
Principal Component Projection Without Principal Component Analysis. CoRR abs/1602.06872 (2016) - [i23]Prateek Jain, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. CoRR abs/1602.06929 (2016) - [i22]Alina Ene, Gary L. Miller, Jakub Pachocki, Aaron Sidford:
Routing under Balance. CoRR abs/1603.09009 (2016) - [i21]Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford:
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. CoRR abs/1604.03930 (2016) - [i20]Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR abs/1605.08754 (2016) - [i19]Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, Aaron Sidford:
Geometric Median in Nearly Linear Time. CoRR abs/1606.05225 (2016) - [i18]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Aaron Sidford, Adrian Vladu:
Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. CoRR abs/1608.03270 (2016) - [i17]Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford:
Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. CoRR abs/1610.03774 (2016) - [i16]Deeparnab Chakrabarty, Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong:
Subquadratic Submodular Function Minimization. CoRR abs/1610.09800 (2016) - [i15]Jakub Pachocki, Liam Roditty, Aaron Sidford, Roei Tov, Virginia Vassilevska Williams:
Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. CoRR abs/1611.00721 (2016) - [i14]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs. CoRR abs/1611.00755 (2016) - [i13]Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford:
Accelerated Methods for Non-Convex Optimization. CoRR abs/1611.00756 (2016) - 2015
- [b1]Aaron Sidford:
Iterative methods, combinatorial optimization, and linear programming beyond the universal barrier. Massachusetts Institute of Technology, Cambridge, MA, USA, 2015 - [c11]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. COLT 2015: 728-763 - [c10]Yin Tat Lee, Aaron Sidford:
Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. FOCS 2015: 230-249 - [c9]Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong:
A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. FOCS 2015: 1049-1065 - [c8]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. ICML 2015: 2540-2548 - [c7]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. ITCS 2015: 181-190 - [c6]Erik D. Demaine, Tim Kaler, Quanquan C. Liu, Aaron Sidford, Adam Yedidia:
Polylogarithmic Fully Retroactive Priority Queues via Hierarchical Checkpointing. WADS 2015: 263-275 - [i12]Yin Tat Lee, Aaron Sidford:
Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. CoRR abs/1503.01752 (2015) - [i11]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. CoRR abs/1506.07512 (2015) - [i10]Yin Tat Lee, Aaron Sidford, Sam Chiu-wai Wong:
A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. CoRR abs/1508.04874 (2015) - [i9]Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford:
Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. CoRR abs/1510.08896 (2015) - 2014
- [c5]Yin Tat Lee, Aaron Sidford:
Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow. FOCS 2014: 424-433 - [c4]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. FOCS 2014: 561-570 - [c3]Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, Aaron Sidford:
An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. SODA 2014: 217-226 - [i8]Michael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, Aaron Sidford:
Single Pass Spectral Sparsification in Dynamic Streams. CoRR abs/1407.1289 (2014) - [i7]Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, Richard Peng, Aaron Sidford:
Uniform Sampling for Matrix Approximation. CoRR abs/1408.5099 (2014) - [i6]Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford:
Competing with the Empirical Risk Minimizer in a Single Pass. CoRR abs/1412.6606 (2014) - 2013
- [c2]Yin Tat Lee, Aaron Sidford:
Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. FOCS 2013: 147-156 - [c1]Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, Zeyuan Allen Zhu:
A simple, combinatorial algorithm for solving SDD systems in nearly-linear time. STOC 2013: 911-920 - [i5]Jonathan A. Kelner, Lorenzo Orecchia, Aaron Sidford, Zeyuan Allen Zhu:
A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time. CoRR abs/1301.6628 (2013) - [i4]Jonathan A. Kelner, Lorenzo Orecchia, Yin Tat Lee, Aaron Sidford:
An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. CoRR abs/1304.2338 (2013) - [i3]Yin Tat Lee, Aaron Sidford:
Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. CoRR abs/1305.1922 (2013) - [i2]Yin Tat Lee, Aaron Sidford:
Matching the Universal Barrier Without Paying the Costs : Solving Linear Programs with Õ(sqrt(rank)) Linear System Solves. CoRR abs/1312.6677 (2013) - [i1]Yin Tat Lee, Aaron Sidford:
Following the Path of Least Resistance : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem. CoRR abs/1312.6713 (2013)
Coauthor Index
[c115] [c114] [j10] [c111] [c106] [c104] [i114] [i112] [i110] [i109] [c99] [c98] [c90] [c87] [i108] [i105] [i96] [i94] [i93] [c80] [c79] [c78] [c76] [c73] [c72] [i87] [i86] [i85] [i83] [c70] [c67] [c66] [i79] [i77] [i75] [i73] [c55] [i70] [i58] [c43] [c41] [c37] [i52] [i51] [c24] [i38] [i37] [i34] [i26]
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