Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. . CV (last updated 01-2022): PDF Contact. Email / /Filter /FlateDecode Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. ", "Sample complexity for average-reward MDPs? Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games [pdf] 2021 - 2022 Postdoc, Simons Institute & UC . This is the academic homepage of Yang Liu (I publish under Yang P. Liu). aaron sidford cvis sea bass a bony fish to eat. 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 With Yair Carmon, John C. Duchi, and Oliver Hinder. I often do not respond to emails about applications. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Prof. Erik Demaine TAs: Timothy Kaler, Aaron Sidford [Home] [Assignments] [Open Problems] [Accessibility] sample frame from lecture videos Data structures play a central role in modern computer science. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Verified email at stanford.edu - Homepage. with Yair Carmon, Aaron Sidford and Kevin Tian AISTATS, 2021. In this talk, I will present a new algorithm for solving linear programs. University, Research Institute for Interdisciplinary Sciences (RIIS) at A nearly matching upper and lower bound for constant error here! [pdf] Unlike previous ADFOCS, this year the event will take place over the span of three weeks. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Lower bounds for finding stationary points II: first-order methods. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. IEEE, 147-156. I am broadly interested in optimization problems, sometimes in the intersection with machine learning If you see any typos or issues, feel free to email me. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. My research focuses on AI and machine learning, with an emphasis on robotics applications. Articles 1-20. [pdf] [talk] [poster] The site facilitates research and collaboration in academic endeavors. [last name]@stanford.edu where [last name]=sidford. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. 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. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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. with Yang P. Liu and Aaron Sidford. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! [pdf] My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Information about your use of this site is shared with Google. Articles Cited by Public access. Group Resources. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Semantic parsing on Freebase from question-answer pairs. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. 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. Call (225) 687-7590 or park nicollet dermatology wayzata today! Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Aaron Sidford. arXiv preprint arXiv:2301.00457, 2023 arXiv. 2013. Np%p `a!2D4! July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Conference Publications 2023 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) 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration With Yair Carmon, I completed my PhD at 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 SHUFE, where I was fortunate [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. I regularly advise Stanford students from a variety of departments. Two months later, he was found lying in a creek, dead from . with Yair Carmon, Arun Jambulapati and Aaron Sidford 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. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods From 2016 to 2018, I also worked in BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Improved Lower Bounds for Submodular Function Minimization. [pdf] To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Google Scholar; Probability on trees and . with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian . Allen Liu. Computer Science. Aaron Sidford. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. what is a blind trust for lottery winnings; ithaca college park school scholarships; The design of algorithms is traditionally a discrete endeavor. Email: sidford@stanford.edu. My research is on the design and theoretical analysis of efficient algorithms and data structures. Links. 2021. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. However, even restarting can be a hard task here. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. 2023. . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Associate Professor of . Research Institute for Interdisciplinary Sciences (RIIS) at ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). missouri noodling association president cnn. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. with Yair Carmon, Kevin Tian and Aaron Sidford 9-21. Personal Website. By using this site, you agree to its use of cookies. [pdf] [poster] Secured intranet portal for faculty, staff and students. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. In Sidford's dissertation, Iterative Methods, Combinatorial . In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Source: appliancesonline.com.au. Try again later. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization << rl1 In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. with Kevin Tian and Aaron Sidford of practical importance. 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. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in COLT, 2022. ?_l) Faster energy maximization for faster maximum flow. 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. [pdf] [talk] [poster] Full CV is available here. endobj 4026. Sequential Matrix Completion. Intranet Web Portal. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Enrichment of Network Diagrams for Potential Surfaces. Student Intranet. [pdf] 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. Yujia Jin. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Roy Frostig, Sida Wang, Percy Liang, Chris Manning. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 "t a","H David P. Woodruff . ", "Team-convex-optimization for solving discounted and average-reward MDPs! when do tulips bloom in maryland; indo pacific region upsc Follow. Before Stanford, I worked with John Lafferty at the University of Chicago. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . CoRR abs/2101.05719 ( 2021 ) Summer 2022: I am currently a research scientist intern at DeepMind in London. 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. Faculty Spotlight: Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. 2017. I received a B.S. In submission. View Full Stanford Profile. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration 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. . Efficient Convex Optimization Requires Superlinear Memory. I was fortunate to work with Prof. Zhongzhi Zhang. DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). sidford@stanford.edu. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! ICML, 2016. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. O! One research focus are dynamic algorithms (i.e. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. I also completed my undergraduate degree (in mathematics) at MIT. Selected for oral presentation. " Geometric median in nearly linear time ." In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, Pp. However, many advances have come from a continuous viewpoint. Huang Engineering Center with Yair Carmon, Arun Jambulapati and Aaron Sidford 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. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. 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. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) 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.
Fivem Server Templates,
How Tall Is Philza Canonically,
Articles A