Lower bounds for finding stationary points II: first-order methods. F+s9H Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. [pdf] ! ICML, 2016. Aaron Sidford Stanford University Verified email at stanford.edu. with Yair Carmon, Kevin Tian and Aaron Sidford This is the academic homepage of Yang Liu (I publish under Yang P. Liu). We also provide two . % [pdf] Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Two months later, he was found lying in a creek, dead from . Optimization and Algorithmic Paradigms (CS 261): Winter '23, Optimization Algorithms (CS 369O / CME 334 / MS&E 312): Fall '22, Discrete Mathematics and Algorithms (CME 305 / MS&E 315): Winter '22, '21, '20, '19, '18, Introduction to Optimization Theory (CS 269O / MS&E 213): Fall '20, '19, Spring '19, '18, '17, Almost Linear Time Graph Algorithms (CS 269G / MS&E 313): Fall '18, Winter '17. University of Cambridge MPhil. {{{;}#q8?\. 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. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Neural Information Processing Systems (NeurIPS), 2014. My long term goal is to bring robots into human-centered domains such as homes and hospitals. I am fortunate to be advised by Aaron Sidford . With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Personal Website. 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. Email: [name]@stanford.edu Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization /N 3 Faculty Spotlight: Aaron Sidford. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Improved Lower Bounds for Submodular Function Minimization. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. 2013. Stanford, CA 94305 Before attending Stanford, I graduated from MIT in May 2018. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. AISTATS, 2021. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Email / Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games [pdf] Yang P. Liu, Aaron Sidford, Department of Mathematics I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. View Full Stanford Profile. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. 4026. sidford@stanford.edu. endobj I am broadly interested in mathematics and theoretical computer science. what is a blind trust for lottery winnings; ithaca college park school scholarships; I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Done under the mentorship of M. Malliaris. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford It was released on november 10, 2017. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. . (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. I am Navajo Math Circles Instructor. One research focus are dynamic algorithms (i.e. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. theses are protected by copyright. (ACM Doctoral Dissertation Award, Honorable Mention.) However, many advances have come from a continuous viewpoint. Our method improves upon the convergence rate of previous state-of-the-art linear programming . I was fortunate to work with Prof. Zhongzhi Zhang. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! In this talk, I will present a new algorithm for solving linear programs. 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. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). [pdf] [slides] ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Goethe University in Frankfurt, Germany. Page 1 of 5 Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science CONTACT INFORMATION Administrative Contact Jackie Nguyen - Administrative Associate Here are some lecture notes that I have written over the years. pdf, Sequential Matrix Completion. in math and computer science from Swarthmore College in 2008. Main Menu. Anup B. Rao. . Honorable Mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. with Yang P. Liu and Aaron Sidford. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. If you see any typos or issues, feel free to email me. . Lower bounds for finding stationary points I, Accelerated Methods for NonConvex Optimization, SIAM Journal on Optimization, 2018 (arXiv), Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. I regularly advise Stanford students from a variety of departments. %PDF-1.4 ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Algorithms Optimization and Numerical Analysis. 475 Via Ortega when do tulips bloom in maryland; indo pacific region upsc ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. by Aaron Sidford. I am an Assistant Professor in the School of Computer Science at Georgia Tech. University, where which is why I created a 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. how . [pdf] [talk] With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Try again later. 5 0 obj ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! IEEE, 147-156. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Their, This "Cited by" count includes citations to the following articles in Scholar. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. The paper, Efficient Convex Optimization Requires Superlinear Memory, was co-authored with Stanford professor Gregory Valiant as well as current Stanford student Annie Marsden and alumnus Vatsal Sharan. Associate Professor of . Articles 1-20. publications by categories in reversed chronological order. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. 2016. 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. MS&E welcomes new faculty member, Aaron Sidford ! Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Sequential Matrix Completion. rl1 Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. /Filter /FlateDecode 4 0 obj Summer 2022: I am currently a research scientist intern at DeepMind in London. Etude for the Park City Math Institute Undergraduate Summer School. 2021. 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 . In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Efficient Convex Optimization Requires Superlinear Memory. with Aaron Sidford 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 . with Arun Jambulapati, Aaron Sidford and Kevin Tian with Yair Carmon, Arun Jambulapati and Aaron Sidford O! 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Yin Tat Lee and Aaron Sidford. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. aaron sidford cvnatural fibrin removalnatural fibrin removal Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Best Paper Award. 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 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 [pdf] [poster] I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. I hope you enjoy the content as much as I enjoyed teaching the class and if you have questions or feedback on the note, feel free to email me. Publications and Preprints. 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. ReSQueing Parallel and Private Stochastic Convex Optimization. University, Research Institute for Interdisciplinary Sciences (RIIS) at Slides from my talk at ITCS. About Me. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Computer Science. I received a B.S. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Aleksander Mdry; Generalized preconditioning and network flow problems [pdf] [talk] [poster] Some I am still actively improving and all of them I am happy to continue polishing. /Creator (Apache FOP Version 1.0) My research is on the design and theoretical analysis of efficient algorithms and data structures. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Links. [pdf] 113 * 2016: The system can't perform the operation now. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . [pdf] [poster] Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . The site facilitates research and collaboration in academic endeavors. Intranet Web Portal. 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. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Contact. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Improves the stochas-tic convex optimization problem in parallel and DP setting. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. ", "Sample complexity for average-reward MDPs? Research Institute for Interdisciplinary Sciences (RIIS) at Secured intranet portal for faculty, staff and students. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. By using this site, you agree to its use of cookies. << David P. Woodruff . [last name]@stanford.edu where [last name]=sidford. If you see any typos or issues, feel free to email me. [pdf] [talk] [poster] Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. with Aaron Sidford In International Conference on Machine Learning (ICML 2016). Another research focus are optimization algorithms. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Before Stanford, I worked with John Lafferty at the University of Chicago. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Try again later. SODA 2023: 5068-5089. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. 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.
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