with Aaron Sidford
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UGTCS Enrichment of Network Diagrams for Potential Surfaces. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Secured intranet portal for faculty, staff and students. I am broadly interested in optimization problems, sometimes in the intersection with machine learning
University, Research Institute for Interdisciplinary Sciences (RIIS) at
In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. Student Intranet. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs.
Aaron Sidford - Home - Author DO Series Efficient Convex Optimization Requires Superlinear Memory. (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, 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, 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, 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), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, 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, 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), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, 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), 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, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods.
NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games
Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Title. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
AISTATS, 2021.
Aaron Sidford - Teaching Sequential Matrix Completion.
Aaron Sidford - Selected Publications with Yair Carmon, Arun Jambulapati and Aaron Sidford
", "A short version of the conference publication under the same title. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) ", Applied Math at Fudan
SHUFE, where I was fortunate
Aaron Sidford.
About Me. Yair Carmon. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. [pdf]
In this talk, I will present a new algorithm for solving linear programs. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. how . Annie Marsden. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. 113 * 2016: The system can't perform the operation now. Best Paper Award. [pdf] [talk]
2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. "FV %H"Hr
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with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford
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Cameron Musco - Manning College of Information & Computer Sciences I received my PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where I was advised by Professor Jonathan Kelner.
[pdf]
(arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. [pdf]
Source: www.ebay.ie I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in
with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford
Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. in Mathematics and B.A. Their, This "Cited by" count includes citations to the following articles in Scholar. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Research Institute for Interdisciplinary Sciences (RIIS) at
He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. << The site facilitates research and collaboration in academic endeavors.
SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. %
Algorithms Optimization and Numerical Analysis. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. small tool to obtain upper bounds of such algebraic algorithms. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
CV (last updated 01-2022): PDF Contact. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space
with Yang P. Liu and Aaron Sidford.
Google Scholar; Probability on trees and . Sidford received his PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner. Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi, Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
>> stream with Yair Carmon, Aaron Sidford and Kevin Tian
I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra.
Allen Liu - GitHub Pages ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. .
I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. /CreationDate (D:20230304061109-08'00') Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. Some I am still actively improving and all of them I am happy to continue polishing. Verified email at stanford.edu - Homepage. Faster energy maximization for faster maximum flow. July 8, 2022. [pdf]
Aaron's research interests lie in optimization, the theory of computation, and the .
Interior Point Methods for Nearly Linear Time Algorithms | ISL They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Secured intranet portal for faculty, staff and students. ! In Sidford's dissertation, Iterative Methods, Combinatorial . with Aaron Sidford
This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). pdf, Sequential Matrix Completion. Publications and Preprints. Management Science & Engineering We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . She was 19 years old and looking forward to the start of classes and reuniting with her college pals.
[1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions
I am fortunate to be advised by Aaron Sidford. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. 2021 - 2022 Postdoc, Simons Institute & UC . Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Faculty Spotlight: Aaron Sidford.
PDF Daogao Liu aaron sidford cv ", "Team-convex-optimization for solving discounted and average-reward MDPs! Before attending Stanford, I graduated from MIT in May 2018. Semantic parsing on Freebase from question-answer pairs. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . Abstract.
MS&E welcomes new faculty member, Aaron Sidford !
Vatsal Sharan - GitHub Pages I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms.
2015 Doctoral Dissertation Award - Association for Computing Machinery Computer Science. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Email: sidford@stanford.edu.
[pdf] [talk]
[pdf] [talk] [poster]
", "A general continuous optimization framework for better dynamic (decremental) matching algorithms.
with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian
Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova .
en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers).
Aviv Tamar - Reinforcement Learning Research Labs - Technion .
by Aaron Sidford. SODA 2023: 4667-4767. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods
Personal Website. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems.
COLT, 2022.
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CME 305/MS&E 316: Discrete Mathematics and Algorithms ICML, 2016. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Try again later. Mail Code. If you see any typos or issues, feel free to email me. The Complexity of Infinite-Horizon General-Sum Stochastic Games, With Yujia Jin, Vidya Muthukumar, Aaron Sidford, To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear 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, 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, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, 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, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, 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, 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, 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, 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, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-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, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), 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), Instance Based Approximations to Profile Maximum Likelihood, 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), 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), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, 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).