Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. pdf, Sequential Matrix Completion. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. ", "Team-convex-optimization for solving discounted and average-reward MDPs! 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. endobj with Aaron Sidford with Yair Carmon, Aaron Sidford and Kevin Tian missouri noodling association president cnn. Semantic parsing on Freebase from question-answer pairs. [pdf] [talk] [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. We forward in this generation, Triumphantly. University of Cambridge MPhil. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Huang Engineering Center . 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. University, Research Institute for Interdisciplinary Sciences (RIIS) at I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. In this talk, I will present a new algorithm for solving linear programs. which is why I created a F+s9H arXiv preprint arXiv:2301.00457, 2023 arXiv. Group Resources. Sequential Matrix Completion. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Thesis, 2016. pdf. However, even restarting can be a hard task here. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions 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. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Aaron Sidford. Call (225) 687-7590 or park nicollet dermatology wayzata today! 475 Via Ortega Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. with Vidya Muthukumar and Aaron Sidford I am an Assistant Professor in the School of Computer Science at Georgia Tech. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). ReSQueing Parallel and Private Stochastic Convex Optimization. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Student Intranet. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Selected recent papers . with Yang P. Liu and Aaron Sidford. Here is a slightly more formal third-person biography, and here is a recent-ish CV. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Information about your use of this site is shared with Google. Improved Lower Bounds for Submodular Function Minimization. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. I enjoy understanding the theoretical ground of many algorithms that are With Yair Carmon, John C. Duchi, and Oliver Hinder. when do tulips bloom in maryland; indo pacific region upsc SODA 2023: 5068-5089. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. 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 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 . I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Done under the mentorship of M. Malliaris. KTH in Stockholm, Sweden, and my BSc + MSc at the D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. 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. [pdf] In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. to be advised by Prof. Dongdong Ge. Summer 2022: I am currently a research scientist intern at DeepMind in London. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. with Kevin Tian and Aaron Sidford ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). with Arun Jambulapati, Aaron Sidford and Kevin Tian Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . ?_l) Journal of Machine Learning Research, 2017 (arXiv). Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. View Full Stanford Profile. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Source: www.ebay.ie [pdf] [poster] " 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. Aaron's research interests lie in optimization, the theory of computation, and the . [pdf] [poster] [pdf] It was released on november 10, 2017. Conference on Learning Theory (COLT), 2015. O! to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. sidford@stanford.edu. rl1 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. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. with Yair Carmon, Aaron Sidford and Kevin Tian Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Abstract. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. what is a blind trust for lottery winnings; ithaca college park school scholarships; Slides from my talk at ITCS. 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. Stanford University 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. Publications and Preprints. Stanford University. /Creator (Apache FOP Version 1.0) If you see any typos or issues, feel free to email me. I also completed my undergraduate degree (in mathematics) at MIT. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Verified email at stanford.edu - Homepage. 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. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford [pdf] [poster] NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Title. 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). 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 . Office: 380-T with Yair Carmon, Arun Jambulapati and Aaron Sidford ", Applied Math at Fudan Email: sidford@stanford.edu. Contact. theory and graph applications. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. I often do not respond to emails about applications. Yujia Jin. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. In submission. 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. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. 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. ", "A short version of the conference publication under the same title. I completed my PhD at About Me. [pdf] [last name]@stanford.edu where [last name]=sidford. The design of algorithms is traditionally a discrete endeavor. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Yin Tat Lee and Aaron Sidford. The following articles are merged in Scholar. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space SODA 2023: 4667-4767. . with Aaron Sidford } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Email / We also provide two . Navajo Math Circles Instructor. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 2016. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). However, many advances have come from a continuous viewpoint. . "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. 4 0 obj Links. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . 2016. 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 . Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 2023. . The system can't perform the operation now. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Faculty Spotlight: Aaron Sidford. The authors of most papers are ordered alphabetically. 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. /N 3 Faster energy maximization for faster maximum flow. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). SHUFE, where I was fortunate ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Here are some lecture notes that I have written over the years. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Email: [name]@stanford.edu ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. % (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, In Symposium on Foundations of Computer Science (FOCS 2015), Machtey Award for Best Student Paper (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv), Competing with the Empirical Risk Minimizer in a Single Pass, With Roy Frostig, Rong Ge, and Sham Kakade, In Conference on Learning Theory (COLT 2015) (arXiv), Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, In International Conference on Machine Learning (ICML 2015) (arXiv), Uniform Sampling for Matrix Approximation, With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng, In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2014), Best Paper Award and Machtey Award for Best Student Paper (arXiv), Single Pass Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco, An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations, With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia, In Symposium on Discrete Algorithms (SODA 2014), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv), A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time, With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu, In Symposium on the Theory of Computing (STOC 2013) (arXiv), SIAM Journal on Computing (arXiv before merge), Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space, With Jack Murtagh, Omer Reingold, and Salil Vadhan, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. 113 * 2016: The system can't perform the operation now. [pdf] [poster] Personal Website. 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. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I graduated with a PhD from Princeton University in 2018. 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 Multiple Scales. resume/cv; publications. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. /Producer (Apache FOP Version 1.0) Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 9-21. STOC 2023. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG CV (last updated 01-2022): PDF Contact. The site facilitates research and collaboration in academic endeavors. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. A nearly matching upper and lower bound for constant error here! Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Secured intranet portal for faculty, staff and students. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Articles 1-20. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Main Menu. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Alcatel flip phones are also ready to purchase with consumer cellular. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. AISTATS, 2021. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. . [pdf] [talk] [poster] Aaron Sidford Stanford University Verified email at stanford.edu. Neural Information Processing Systems (NeurIPS), 2014. 2013. MS&E welcomes new faculty member, Aaron Sidford ! ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 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 I am broadly interested in optimization problems, sometimes in the intersection with machine learning Before Stanford, I worked with John Lafferty at the University of Chicago. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). [pdf] [talk] [poster] Try again later. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. the Operations Research group. [pdf] [talk] IEEE, 147-156. I am theses are protected by copyright. /CreationDate (D:20230304061109-08'00') with Aaron Sidford 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 Here are some lecture notes that I have written over the years. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs << Algorithms Optimization and Numerical Analysis. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). AISTATS, 2021. I am broadly interested in mathematics and theoretical computer science. By using this site, you agree to its use of cookies. Their, This "Cited by" count includes citations to the following articles in Scholar. 2017. 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. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan