← Authors

Aaron Sidford

48 papers · 2015–2024 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (9) 🌈 Renaissance Researcher (7) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (9) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer πŸ”¬ Deep Specialist (25) πŸ† Keyword Champion (2) 🀝 Dynamic Duo (14) 🧬 Topic Evolution πŸ—ƒοΈ Keyword Collector (186) ⚑ Prolific Year (7) πŸ“ˆ Trend Setter πŸ’Ž Century Club (48) πŸ”₯ Unstoppable (10)

Conferences

NIPS (19) COLT (16) ICML (9) AISTATS (1) ALT (1) IJCAI (1) JMLR (1)

Top co-authors

Yujia Jin (14) Kevin Tian (9) Arun Jambulapati (8) Yair Carmon (8) Praneeth Netrapalli (6) Yin Tat Lee (5) Kirankumar Shiragur (5) Qijia Jiang (4) Sham M. Kakade (4) Christopher Musco (4)

Keywords

convex optimization (13) sample complexity (7) gradient descent (5) matrix approximation (5) query complexity (4) generative model (4) profile maximum likelihood (4) stochastic optimization (3) oracle complexity (3) matrix computation (3) first-order method (3) markov decision process (3) empirical risk minimization (3) accelerated gradient (3) distribution estimation (3) variance reduction (3) logistic regression (2) principal component analysis (2) reinforcement learning (2) optimal policy (2)

Papers

Truncated Variance Reduced Value Iteration NIPS 2024
Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization COLT 2024
Faster Spectral Density Estimation and Sparsification in the Nuclear Norm (Extended Abstract) COLT 2024
Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting NIPS 2024
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling ICML 2023
Structured Semidefinite Programming for Recovering Structured Preconditioners NIPS 2023
Quantum speedups for stochastic optimization NIPS 2023
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract) IJCAI 2023
Parallel Submodular Function Minimization NIPS 2023
Semi-Random Sparse Recovery in Nearly-Linear Time COLT 2023
Moments, Random Walks, and Limits for Spectrum Approximation COLT 2023
Towards Optimal Effective Resistance Estimation NIPS 2023
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods COLT 2022
Efficient Convex Optimization Requires Superlinear Memory COLT 2022
On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood NIPS 2022
Optimal and Adaptive Monteiro-Svaiter Acceleration NIPS 2022
RECAPP: Crafting a More Efficient Catalyst for Convex Optimization ICML 2022
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales COLT 2022
The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood COLT 2021
Stochastic Bias-Reduced Gradient Methods NIPS 2021
Towards Tight Bounds on the Sample Complexity of Average-reward MDPs ICML 2021
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss COLT 2021
Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond COLT 2020
Efficiently Solving MDPs with Stochastic Mirror Descent ICML 2020
Instance Based Approximations to Profile Maximum Likelihood NIPS 2020
Acceleration with a Ball Optimization Oracle NIPS 2020
Large-Scale Methods for Distributionally Robust Optimization NIPS 2020
Leverage Score Sampling for Faster Accelerated Regression and ERM ALT 2020
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity AISTATS 2020
A General Framework for Symmetric Property Estimation NIPS 2019
A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport NIPS 2019
Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG NIPS 2019
Complexity of Highly Parallel Non-Smooth Convex Optimization NIPS 2019
Variance Reduction for Matrix Games NIPS 2019
Near-optimal method for highly smooth convex optimization COLT 2019
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives COLT 2019
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification JMLR 2018
Accelerating Stochastic Gradient Descent for Least Squares Regression COLT 2018
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression NIPS 2018
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model NIPS 2018
Efficient Convex Optimization with Membership Oracles COLT 2018
β€œConvex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions ICML 2017
Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016
Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis ICML 2016
Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja’s Algorithm COLT 2016
Principal Component Projection Without Principal Component Analysis ICML 2016
Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization ICML 2015
Competing with the Empirical Risk Minimizer in a Single Pass COLT 2015