My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). in math and computer science from Swarthmore College in 2008.
Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs
I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. The site facilitates research and collaboration in academic endeavors. resume/cv; publications. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 113 * 2016: The system can't perform the operation now. I completed my PhD at
Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. arXiv | conference pdf (alphabetical authorship) Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with . Goethe University in Frankfurt, Germany.
If you see any typos or issues, feel free to email me. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization
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), Subquadratic Submodular Function Minimization, With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong, In Symposium on Theory of Computing (STOC 2017) (arXiv), 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), With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki, In Symposium on Theory of Computing (STOC 2016) (arXiv), With Alina Ene, Gary L. Miller, and Jakub Pachocki, 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, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent
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", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). aaron sidford cvnatural fibrin removalnatural fibrin removal O! Some I am still actively improving and all of them I am happy to continue polishing.
I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Authors: Michael B. Cohen, Jonathan Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford Download PDF Abstract: We show how to solve directed Laplacian systems in nearly-linear time. The authors of most papers are ordered alphabetically. Yair Carmon. 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 . In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. how . SODA 2023: 4667-4767.
Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games
In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. [pdf] [talk]
With Yair Carmon, John C. Duchi, and Oliver Hinder. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Sequential Matrix Completion.
University, Research Institute for Interdisciplinary Sciences (RIIS) at
To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. BayLearn, 2019, "Computing stationary solution for multi-agent RL is hard: Indeed, CCE for simultaneous games and NE for turn-based games are both PPAD-hard.
February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 with Yang P. Liu and Aaron Sidford. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. arXiv | conference pdf (alphabetical authorship), Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with Multiple Scales. University, where
Summer 2022: I am currently a research scientist intern at DeepMind in London. We also provide two . with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford
International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle
Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Best Paper Award. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. This site uses cookies from Google to deliver its services and to analyze traffic. University of Cambridge MPhil. Email /
This work presents an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second derivatives that is Hessian free, i.e., it only requires gradient computations, and is therefore suitable for large-scale applications. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). Google Scholar, The Complexity of Infinite-Horizon General-Sum Stochastic Games, The Complexity of Optimizing Single and Multi-player Games, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions, On the Sample Complexity for Average-reward Markov Decision Processes, Stochastic Methods for Matrix Games and its Applications, Acceleration with a Ball Optimization Oracle, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, The Complexity of Infinite-Horizon General-Sum Stochastic Games
"I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. About Me. Improved Lower Bounds for Submodular Function Minimization. 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. Mail Code. 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. From 2016 to 2018, I also worked in
I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in
[pdf] [poster]
Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space
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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. Assistant Professor of Management Science and Engineering and of Computer Science. I was fortunate to work with Prof. Zhongzhi Zhang. of practical importance. [pdf] [poster]
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Bw!hz#0 )l`/8p.7p|O~ (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. View Full Stanford Profile. aaron sidford cvis sea bass a bony fish to eat. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. 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. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . the Operations Research group. << Before attending Stanford, I graduated from MIT in May 2018.
with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford
With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Aaron Sidford's 143 research works with 2,861 citations and 1,915 reads, including: Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). stream Semantic parsing on Freebase from question-answer pairs. with Aaron Sidford
Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration!
", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! with Vidya Muthukumar and Aaron Sidford
van vu professor, yale Verified email at yale.edu. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group.
In each setting we provide faster exact and approximate algorithms. In submission.
With Cameron Musco and Christopher Musco. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). In International Conference on Machine Learning (ICML 2016). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). The following articles are merged in Scholar. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra.