In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . O! 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 riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries [pdf] [slides] Verified email at stanford.edu - Homepage. Try again later. Page 1 of 5 Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science CONTACT INFORMATION Administrative Contact Jackie Nguyen - Administrative Associate Selected for oral presentation. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. I am fortunate to be advised by Aaron Sidford. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. I enjoy understanding the theoretical ground of many algorithms that are My research is on the design and theoretical analysis of efficient algorithms and data structures. 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. Here are some lecture notes that I have written over the years. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Source: appliancesonline.com.au. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Some I am still actively improving and all of them I am happy to continue polishing. 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. COLT, 2022. Another research focus are optimization algorithms. I graduated with a PhD from Princeton University in 2018. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games . Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I am broadly interested in mathematics and theoretical computer science. ! in Chemistry at the University of Chicago. 475 Via Ortega Call (225) 687-7590 or park nicollet dermatology wayzata today! 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. Some I am still actively improving and all of them I am happy to continue polishing. /Producer (Apache FOP Version 1.0) In submission. "t a","H My CV. The site facilitates research and collaboration in academic endeavors. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? This site uses cookies from Google to deliver its services and to analyze traffic. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. endobj By using this site, you agree to its use of cookies. [email protected]. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization [pdf] [talk] [poster] I regularly advise Stanford students from a variety of departments. Abstract. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Articles 1-20. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. ", Applied Math at Fudan small tool to obtain upper bounds of such algebraic algorithms. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Enrichment of Network Diagrams for Potential Surfaces. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. We also provide two . Office: 380-T D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 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. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. %PDF-1.4 ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). 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. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. University of Cambridge MPhil. It was released on november 10, 2017. Email: [email protected]. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. The authors of most papers are ordered alphabetically. Semantic parsing on Freebase from question-answer pairs. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. 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. The following articles are merged in Scholar. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Publications and Preprints. 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. 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 . [pdf] Aaron Sidford. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. 4026. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. how . ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Yin Tat Lee and Aaron Sidford. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. with Aaron Sidford A nearly matching upper and lower bound for constant error here! Email: [name]@stanford.edu [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Here is a slightly more formal third-person biography, and here is a recent-ish CV. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. 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. 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. Alcatel flip phones are also ready to purchase with consumer cellular. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. AISTATS, 2021. [pdf] [talk] [poster] (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Secured intranet portal for faculty, staff and students. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). /Filter /FlateDecode My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Improved Lower Bounds for Submodular Function Minimization. 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). arXiv preprint arXiv:2301.00457, 2023 arXiv. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford University, Research Institute for Interdisciplinary Sciences (RIIS) at [pdf] [talk] [poster] Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. David P. Woodruff . Etude for the Park City Math Institute Undergraduate Summer School. Aaron Sidford ([email protected]) 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. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! I received a B.S. [pdf] . resume/cv; publications. [pdf] [poster] 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. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. 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. Associate Professor of . [pdf] Management Science & Engineering Conference on Learning Theory (COLT), 2015. (ACM Doctoral Dissertation Award, Honorable Mention.) ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! I am 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. rl1 Allen Liu. 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 . Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Links. However, many advances have come from a continuous viewpoint. I am fortunate to be advised by Aaron Sidford . Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. with Yair Carmon, Kevin Tian and Aaron Sidford with Vidya Muthukumar and Aaron Sidford Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. 2017. United States. [pdf] [poster] Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Google Scholar; Probability on trees and . Our method improves upon the convergence rate of previous state-of-the-art linear programming . /CreationDate (D:20230304061109-08'00') with 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. theses are protected by copyright. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games 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. when do tulips bloom in maryland; indo pacific region upsc The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Lower bounds for finding stationary points II: first-order methods. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Contact. 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, I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. [pdf] 2013. Email / My long term goal is to bring robots into human-centered domains such as homes and hospitals. Anup B. Rao. Simple MAP inference via low-rank relaxations. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. SODA 2023: 4667-4767. Selected recent papers . I was fortunate to work with Prof. Zhongzhi Zhang. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford I am broadly interested in mathematics and theoretical computer science. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Journal of Machine Learning Research, 2017 (arXiv). Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Intranet Web Portal. ICML, 2016. (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. 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 . I completed my PhD at Nearly Optimal Communication and Query Complexity of Bipartite Matching . Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . I am an Assistant Professor in the School of Computer Science at Georgia Tech. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. F+s9H {{{;}#q8?\. With Cameron Musco and Christopher Musco. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. aaron sidford cvnatural fibrin removalnatural fibrin removal In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Full CV is available here. Google Scholar Digital Library; Russell Lyons and Yuval Peres. 2023. . If you see any typos or issues, feel free to email me. View Full Stanford Profile. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods 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. 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. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 I am broadly interested in optimization problems, sometimes in the intersection with machine learning Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! 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. Slides from my talk at ITCS. Efficient Convex Optimization Requires Superlinear Memory. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 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. 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. Aleksander Mdry; Generalized preconditioning and network flow problems Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. University, where 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. [last name]@stanford.edu where [last name]=sidford. CoRR abs/2101.05719 ( 2021 ) Faculty Spotlight: Aaron Sidford. Title. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. I often do not respond to emails about applications. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Goethe University in Frankfurt, Germany. 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

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