In submission. I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. ReSQueing Parallel and Private Stochastic Convex Optimization. Contact. Instructor: Aaron Sidford Winter 2018 Time: Tuesdays and Thursdays, 10:30 AM - 11:50 AM Room: Education Building, Room 128 Here is the course syllabus. Source: appliancesonline.com.au. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Online Edge Coloring via Tree Recurrences and Correlation Decay, STOC 2022 IEEE, 147-156. Neural Information Processing Systems (NeurIPS), 2014. [pdf] [talk] [poster] In each setting we provide faster exact and approximate algorithms. The following articles are merged in Scholar. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. I also completed my undergraduate degree (in mathematics) at MIT. [pdf] [poster] CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. [pdf] stream 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Improves the stochas-tic convex optimization problem in parallel and DP setting. Try again later. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. SODA 2023: 4667-4767. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . AISTATS, 2021. with Arun Jambulapati, Aaron Sidford and Kevin Tian Two months later, he was found lying in a creek, dead from . 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. 2022 - Learning and Games Program, Simons Institute, Sept. 2021 - Young Researcher Workshop, Cornell ORIE, Sept. 2021 - ACO Student Seminar, Georgia Tech, Dec. 2019 - NeurIPS Spotlight presentation. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Research Institute for Interdisciplinary Sciences (RIIS) at 2016. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. (ACM Doctoral Dissertation Award, Honorable Mention.) Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 1 -Regression in Nearly Linear Time for Dense Instances. arXiv preprint arXiv:2301.00457, 2023 arXiv. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Thesis, 2016. pdf. Stanford University Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. I enjoy understanding the theoretical ground of many algorithms that are Some I am still actively improving and all of them I am happy to continue polishing. My CV. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Main Menu. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. >> [pdf] 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. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Google Scholar Digital Library; Russell Lyons and Yuval Peres. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [pdf] [talk] sidford@stanford.edu. with Kevin Tian and Aaron Sidford 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. 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. By using this site, you agree to its use of cookies. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Office: 380-T There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Before joining Stanford in Fall 2016, I was an NSF post-doctoral fellow at Carnegie Mellon University ; I received a Ph.D. in mathematics from the University of Michigan in 2014, and a B.A. Sequential Matrix Completion. %PDF-1.4 Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. . with Vidya Muthukumar and Aaron Sidford We forward in this generation, Triumphantly. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). with Yair Carmon, Aaron Sidford and Kevin Tian Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) University, Research Institute for Interdisciplinary Sciences (RIIS) at [pdf] Slides from my talk at ITCS. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. This site uses cookies from Google to deliver its services and to analyze traffic. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. [pdf] We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. University, where Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. [pdf] [slides] the Operations Research group. Efficient Convex Optimization Requires Superlinear Memory. University of Cambridge MPhil. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. In International Conference on Machine Learning (ICML 2016). Navajo Math Circles Instructor. Full CV is available here. Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is . F+s9H {{{;}#q8?\. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Yujia Jin. with Aaron Sidford . Allen Liu. Articles Cited by Public access. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 with Aaron Sidford [pdf] With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Aleksander Mdry; Generalized preconditioning and network flow problems Yin Tat Lee and Aaron Sidford. CV (last updated 01-2022): PDF Contact. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. 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. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. If you see any typos or issues, feel free to email me. The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries With Cameron Musco and Christopher Musco. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Source: www.ebay.ie Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Applying this technique, we prove that any deterministic SFM algorithm . I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Journal of Machine Learning Research, 2017 (arXiv). (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. ", Applied Math at Fudan with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. "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. [pdf] [poster] I often do not respond to emails about applications. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). ", "A short version of the conference publication under the same title. 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. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). With Jack Murtagh, Omer Reingold, and Salil P. Vadhan.
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