in math and computer science from Swarthmore College in 2008. . Assistant Professor of Management Science and Engineering and of Computer Science. "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. Goethe University in Frankfurt, Germany. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University in Mathematics and B.A. Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian . 5 0 obj I am fortunate to be advised by Aaron Sidford. Stanford University by Aaron Sidford. I received a B.S. In this talk, I will present a new algorithm for solving linear programs. /N 3 In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 475 Via Ortega 2015 Doctoral Dissertation Award - Association for Computing Machinery Cameron Musco - Manning College of Information & Computer Sciences IEEE, 147-156. By using this site, you agree to its use of cookies. SHUFE, where I was fortunate ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. University of Cambridge MPhil. 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, There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. [pdf] [talk] [poster] Aaron Sidford's Homepage - Stanford University with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Annie Marsden. what is a blind trust for lottery winnings; ithaca college park school scholarships; [pdf] [talk] Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Stanford, CA 94305 Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. missouri noodling association president cnn. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Aaron Sidford - All Publications >> However, even restarting can be a hard task here. Try again later. [pdf] 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. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Before attending Stanford, I graduated from MIT in May 2018. Aaron Sidford. In each setting we provide faster exact and approximate algorithms. Articles Cited by Public access. 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. /Creator (Apache FOP Version 1.0) to be advised by Prof. Dongdong Ge. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu CV (last updated 01-2022): PDF Contact. Aaron Sidford. van vu professor, yale Verified email at yale.edu. [PDF] Faster Algorithms for Computing the Stationary Distribution 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. Email / [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. stream of practical importance. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 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. About - Annie Marsden . Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Anup B. Rao - Google Scholar Stanford University. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Iterative methods, combinatorial optimization, and linear programming International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Information about your use of this site is shared with Google. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) 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. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . We forward in this generation, Triumphantly. Title. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle 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. with Vidya Muthukumar and Aaron Sidford SODA 2023: 5068-5089. 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 maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. theory and graph applications. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. 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. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. ", "A short version of the conference publication under the same title. ! Here is a slightly more formal third-person biography, and here is a recent-ish CV. CoRR abs/2101.05719 ( 2021 ) 9-21. Yang P. Liu - GitHub Pages The following articles are merged in Scholar. 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), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020). with Yair Carmon, Aaron Sidford and Kevin Tian CME 305/MS&E 316: Discrete Mathematics and Algorithms Student Intranet. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Aaron Sidford - Home - Author DO Series Done under the mentorship of M. Malliaris. (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Aaron Sidford's research works | Stanford University, CA (SU) and other Their, This "Cited by" count includes citations to the following articles in Scholar. Np%p `a!2D4! Intranet Web Portal. 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. with Aaron Sidford Alcatel One Touch Flip Phone - New Product Recommendations, Promotions ReSQueing Parallel and Private Stochastic Convex Optimization. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Some I am still actively improving and all of them I am happy to continue polishing. Journal of Machine Learning Research, 2017 (arXiv). Navajo Math Circles Instructor. aaron sidford cv Full CV is available here. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Microsoft Research Faculty Fellowship 2020: Researchers in academia at ", Applied Math at Fudan 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. with Yang P. Liu and Aaron Sidford. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Sidford received his PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner. % [pdf] International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Efficient Convex Optimization Requires Superlinear Memory. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." I am fortunate to be advised by Aaron Sidford . Aleksander Mdry; Generalized preconditioning and network flow problems Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. 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 With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Aaron's research interests lie in optimization, the theory of computation, and the . To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Lower bounds for finding stationary points II: first-order methods. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Accelerated Methods for NonConvex Optimization | Semantic Scholar 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. [pdf] July 8, 2022. Faculty Spotlight: Aaron Sidford - Management Science and Engineering /CreationDate (D:20230304061109-08'00') CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Thesis, 2016. pdf. 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 Aaron Sidford | Stanford Online Selected for oral presentation. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Here are some lecture notes that I have written over the years. Sampling random spanning trees faster than matrix multiplication Publications | Salil Vadhan 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. Links. My research is on the design and theoretical analysis of efficient algorithms 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. 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. [last name]@stanford.edu where [last name]=sidford. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). rl1 Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. in Chemistry at the University of Chicago. 2023. . MS&E213 / CS 269O - Introduction to Optimization Theory Our method improves upon the convergence rate of previous state-of-the-art linear programming . This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). In submission. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Faster Matroid Intersection Princeton University Gregory Valiant Homepage - Stanford University Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs with Yair Carmon, Aaron Sidford and Kevin Tian Mail Code. 113 * 2016: The system can't perform the operation now. resume/cv; publications. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games I completed my PhD at 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. 2016. Simple MAP inference via low-rank relaxations. Neural Information Processing Systems (NeurIPS), 2014. /Producer (Apache FOP Version 1.0) With Cameron Musco and Christopher Musco. [pdf] [talk] [poster] Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss

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