Management Science & Engineering ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Publications | Salil Vadhan [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Efficient Convex Optimization Requires Superlinear Memory. small tool to obtain upper bounds of such algebraic algorithms. Faster energy maximization for faster maximum flow. with Yair Carmon, Aaron Sidford and Kevin Tian I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. with Aaron Sidford With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Vatsal Sharan - GitHub Pages 5 0 obj Contact. The authors of most papers are ordered alphabetically. Faculty and Staff Intranet. Information about your use of this site is shared with Google. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. 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 . In submission. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Assistant Professor of Management Science and Engineering and of Computer Science. Yang P. Liu, Aaron Sidford, Department of Mathematics Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. University, Research Institute for Interdisciplinary Sciences (RIIS) at ?_l) dblp: Yin Tat Lee Abstract. Roy Frostig - Stanford University in Chemistry at the University of Chicago. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. what is a blind trust for lottery winnings; ithaca college park school scholarships; Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. with Yair Carmon, Aaron Sidford and Kevin Tian 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, [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. However, many advances have come from a continuous viewpoint. Personal Website. She was 19 years old and looking - freewareppc.com ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. My research was supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship from 2018-2021, and by a Google PhD Fellowship from 2022-2023. Their, This "Cited by" count includes citations to the following articles in Scholar. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Semantic parsing on Freebase from question-answer pairs. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. 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. [email protected]. [pdf] [pdf] Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games theses are protected by copyright. I often do not respond to emails about applications. SODA 2023: 5068-5089. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Aaron Sidford. Here is a slightly more formal third-person biography, and here is a recent-ish CV. Improves the stochas-tic convex optimization problem in parallel and DP setting. Sampling random spanning trees faster than matrix multiplication Google Scholar; Probability on trees and . Links. 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. Aaron Sidford 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. % The system can't perform the operation now. 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. F+s9H . Aaron Sidford's Homepage - Stanford University 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. The design of algorithms is traditionally a discrete endeavor. aaron sidford cv Call (225) 687-7590 or park nicollet dermatology wayzata today! They will share a $10,000 prize, with financial sponsorship provided by Google Inc. I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. >> MS&E213 / CS 269O - Introduction to Optimization Theory arXiv preprint arXiv:2301.00457, 2023 arXiv. Try again later. Yin Tat Lee and Aaron Sidford. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Before attending Stanford, I graduated from MIT in May 2018. 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. We forward in this generation, Triumphantly. AISTATS, 2021. /CreationDate (D:20230304061109-08'00') CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. 2017. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Etude for the Park City Math Institute Undergraduate Summer School. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. My research is on the design and theoretical analysis of efficient algorithms and data structures. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) United States. [pdf] [poster] Two months later, he was found lying in a creek, dead from . "t a","H to be advised by Prof. Dongdong Ge. I also completed my undergraduate degree (in mathematics) at MIT. Goethe University in Frankfurt, Germany. Neural Information Processing Systems (NeurIPS), 2014. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Secured intranet portal for faculty, staff and students. 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 . Aviv Tamar - Reinforcement Learning Research Labs - Technion We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . /N 3 Before Stanford, I worked with John Lafferty at the University of Chicago. [pdf] Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. publications by categories in reversed chronological order. 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 .
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