Student Intranet. 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. We forward in this generation, Triumphantly. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Iterative methods, combinatorial optimization, and linear programming Some I am still actively improving and all of them I am happy to continue polishing. ", "Sample complexity for average-reward MDPs? Aaron Sidford - All Publications Semantic parsing on Freebase from question-answer pairs. Aleksander Mdry; Generalized preconditioning and network flow problems I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Another research focus are optimization algorithms. Faculty Spotlight: Aaron Sidford - Management Science and Engineering Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Aaron Sidford Stanford University Verified email at stanford.edu. 5 0 obj with Yang P. Liu and Aaron Sidford. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. 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. SODA 2023: 5068-5089. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. [pdf] I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. My CV. Information about your use of this site is shared with Google. SHUFE, where I was fortunate 2016. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Management Science & Engineering Improved Lower Bounds for Submodular Function Minimization AISTATS, 2021. << David P. Woodruff - Carnegie Mellon University They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. In each setting we provide faster exact and approximate algorithms. Slides from my talk at ITCS. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. My research is on the design and theoretical analysis of efficient algorithms and data structures. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Follow. 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 This is the academic homepage of Yang Liu (I publish under Yang P. Liu). with Vidya Muthukumar and Aaron Sidford van vu professor, yale Verified email at yale.edu. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. With Cameron Musco and Christopher Musco. 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. he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). Faster Matroid Intersection Princeton University what is a blind trust for lottery winnings; ithaca college park school scholarships; This improves upon previous best known running times of O (nr1.5T-ind) due to Cunningham in 1986 and (n2T-ind+n3) due to Lee, Sidford, and Wong in 2015. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. with Yair Carmon, Arun Jambulapati and Aaron Sidford Nearly Optimal Communication and Query Complexity of Bipartite Matching . Aaron Sidford receives best paper award at COLT 2022 /N 3 With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. I am fortunate to be advised by Aaron Sidford. From 2016 to 2018, I also worked in Try again later. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Allen Liu. The site facilitates research and collaboration in academic endeavors. I regularly advise Stanford students from a variety of departments. 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. [pdf] [talk] [poster] ! Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! /Length 11 0 R International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Aaron Sidford - Teaching 2013. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Associate Professor of . 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 . University of Cambridge MPhil. [pdf] Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Yin Tat Lee and Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. MS&E welcomes new faculty member, Aaron Sidford ! 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. Mary Wootters - Google United States. 4 0 obj with Yair Carmon, Aaron Sidford and Kevin Tian With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Google Scholar; Probability on trees and . [pdf] [poster] 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. Huang Engineering Center Neural Information Processing Systems (NeurIPS), 2014. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Simple MAP inference via low-rank relaxations. Aaron Sidford. [PDF] Faster Algorithms for Computing the Stationary Distribution Aaron Sidford - Home - Author DO Series Before Stanford, I worked with John Lafferty at the University of Chicago. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. I graduated with a PhD from Princeton University in 2018. With Yair Carmon, John C. Duchi, and Oliver Hinder. Alcatel flip phones are also ready to purchase with consumer cellular. [pdf] [poster] {{{;}#q8?\. [pdf] [talk] Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. with Aaron Sidford ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. PDF Daogao Liu Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 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. Yang P. Liu, Aaron Sidford, Department of Mathematics Vatsal Sharan - GitHub Pages ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Email: [name]@stanford.edu 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 ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games UGTCS Assistant Professor of Management Science and Engineering and of Computer Science. with Aaron Sidford } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). 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. Yujia Jin - Stanford University Articles Cited by Public access. [pdf] I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. publications | Daogao Liu 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 KTH in Stockholm, Sweden, and my BSc + MSc at the Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Selected recent papers . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Sampling random spanning trees faster than matrix multiplication I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. resume/cv; publications. Faster energy maximization for faster maximum flow. STOC 2023. publications by categories in reversed chronological order. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries CME 305/MS&E 316: Discrete Mathematics and Algorithms Aaron Sidford. [pdf] [talk] [poster] Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs theses are protected by copyright. COLT, 2022. %PDF-1.4 By using this site, you agree to its use of cookies. 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. Jan van den Brand [pdf] [slides] 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. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Yang P. Liu - GitHub Pages ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Source: appliancesonline.com.au. missouri noodling association president cnn. 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 . theory and graph applications. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Computer Science. 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. Enrichment of Network Diagrams for Potential Surfaces. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Secured intranet portal for faculty, staff and students. 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. Faculty Spotlight: Aaron Sidford. Email / with Yair Carmon, Aaron Sidford and Kevin Tian Interior Point Methods for Nearly Linear Time Algorithms | ISL [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Abstract. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Improves the stochas-tic convex optimization problem in parallel and DP setting. Aviv Tamar - Reinforcement Learning Research Labs - Technion 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.