[pdf] [poster] [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. University of Cambridge MPhil. I regularly advise Stanford students from a variety of departments. [pdf] ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). BayLearn, 2021, On the Sample Complexity of Average-reward MDPs My research focuses on AI and machine learning, with an emphasis on robotics applications. My CV. Aaron Sidford. I am an Assistant Professor in the School of Computer Science at Georgia Tech. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Google Scholar; Probability on trees and . 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 In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. pdf, Sequential Matrix Completion. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Yujia Jin. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. theory and graph applications. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. with Vidya Muthukumar and Aaron Sidford I am fortunate to be advised by Aaron Sidford . We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Try again later. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am broadly interested in mathematics and theoretical computer science. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University 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. Faculty and Staff Intranet. Yujia Jin. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Faculty Spotlight: Aaron Sidford. 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. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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. with Aaron Sidford 113 * 2016: The system can't perform the operation now. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. SODA 2023: 5068-5089. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Yang P. Liu, Aaron Sidford, Department of Mathematics The following articles are merged in Scholar. [pdf] [talk] [poster] in Chemistry at the University of Chicago. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. The authors of most papers are ordered alphabetically. IEEE, 147-156. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian I received my PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where I was advised by Professor Jonathan Kelner. 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 We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . /Length 11 0 R Applying this technique, we prove that any deterministic SFM algorithm . ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Best Paper Award. Main Menu. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. This site uses cookies from Google to deliver its services and to analyze traffic. Student Intranet. [pdf] [talk] [poster] missouri noodling association president cnn. Improves the stochas-tic convex optimization problem in parallel and DP setting. Many of my results use fast matrix multiplication Personal Website. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. ", "A short version of the conference publication under the same title. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. with Aaron Sidford I often do not respond to emails about applications. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. F+s9H Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). Sequential Matrix Completion. 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). We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . of practical importance. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. to be advised by Prof. Dongdong Ge. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. 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. . >> Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 2021. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Office: 380-T July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. 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. . 2013. 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 a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. 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, Full CV is available here. The system can't perform the operation now. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Management Science & Engineering with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. 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. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. 2023. . [pdf] [talk] [poster] how . The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. The design of algorithms is traditionally a discrete endeavor. 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 Yair Carmon, Aaron Sidford and Kevin Tian Call (225) 687-7590 or park nicollet dermatology wayzata today! You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 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 Simple MAP inference via low-rank relaxations. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Semantic parsing on Freebase from question-answer pairs. Journal of Machine Learning Research, 2017 (arXiv). CoRR abs/2101.05719 ( 2021 ) /Creator (Apache FOP Version 1.0) 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.