Etude for the Park City Math Institute Undergraduate Summer School. 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. with Yair Carmon, Aaron Sidford and Kevin Tian SHUFE, where I was fortunate I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in AISTATS, 2021. Main Menu. [pdf] My CV. Slides from my talk at ITCS. The design of algorithms is traditionally a discrete endeavor. SODA 2023: 5068-5089. With Cameron Musco and Christopher Musco. 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 ", Applied Math at Fudan With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). CoRR abs/2101.05719 ( 2021 ) We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . I am broadly interested in mathematics and theoretical computer science. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) [pdf] Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Best Paper Award. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Stanford, CA 94305 " 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. pdf, Sequential Matrix Completion. Assistant Professor of Management Science and Engineering and of Computer Science. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. I often do not respond to emails about applications. Summer 2022: I am currently a research scientist intern at DeepMind in London. 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. Annie Marsden. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. I also completed my undergraduate degree (in mathematics) at MIT. The system can't perform the operation now. Some I am still actively improving and all of them I am happy to continue polishing. 2017. In submission. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). AISTATS, 2021. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. United States. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. ", "Team-convex-optimization for solving discounted and average-reward MDPs! I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. by Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. IEEE, 147-156. In Symposium on Theory of Computing (STOC 2020) (arXiv), Constant Girth Approximation for Directed Graphs in Subquadratic Time, With Shiri Chechik, Yang P. Liu, and Omer Rotem, Leverage Score Sampling for Faster Accelerated Regression and ERM, With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli, In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv), Near-optimal Approximate Discrete and Continuous Submodular Function Minimization, In Symposium on Discrete Algorithms (SODA 2020) (arXiv), Fast and Space Efficient Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos, In Conference on Neural Information Processing Systems (NeurIPS 2019), Complexity of Highly Parallel Non-Smooth Convex Optimization, With Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, A Direct (1/) Iteration Parallel Algorithm for Optimal Transport, In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv), A General Framework for Efficient Symmetric Property Estimation, With Moses Charikar and Kirankumar Shiragur, Parallel Reachability in Almost Linear Work and Square Root Depth, In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv), With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong, Deterministic Approximation of Random Walks in Small Space, With Jack Murtagh, Omer Reingold, and Salil P. Vadhan, In International Workshop on Randomization and Computation (RANDOM 2019), A Rank-1 Sketch for Matrix Multiplicative Weights, With Yair Carmon, John C. Duchi, and Kevin Tian, In Conference on Learning Theory (COLT 2019) (arXiv), Near-optimal method for highly smooth convex optimization, Efficient profile maximum likelihood for universal symmetric property estimation, In Symposium on Theory of Computing (STOC 2019) (arXiv), Memory-sample tradeoffs for linear regression with small error, Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications, With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi, In Symposium on Discrete Algorithms (SODA 2019) (arXiv), Exploiting Numerical Sparsity for Efficient Learning: Faster Eigenvector Computation and Regression, In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv), Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model, With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye, Coordinate Methods for Accelerating Regression and Faster Approximate Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2018), Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations, With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao, In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv), Efficient Convex Optimization with Membership Oracles, In Conference on Learning Theory (COLT 2018) (arXiv), Accelerating Stochastic Gradient Descent for Least Squares Regression, With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli, Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. 4026. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. [pdf] of practical importance. with Kevin Tian and Aaron Sidford with Aaron Sidford . 475 Via Ortega ", "A short version of the conference publication under the same title. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Mail Code. Faculty Spotlight: Aaron Sidford. F+s9H STOC 2023. Email: sidford@stanford.edu. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. van vu professor, yale Verified email at yale.edu. Google Scholar; Probability on trees and . which is why I created a Simple MAP inference via low-rank relaxations. 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. Faculty and Staff Intranet. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, 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. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. 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). Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Call (225) 687-7590 or park nicollet dermatology wayzata today! With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. 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. My research is on the design and theoretical analysis of efficient algorithms and data structures. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Aaron Sidford. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 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, Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Efficient Convex Optimization Requires Superlinear Memory. Before attending Stanford, I graduated from MIT in May 2018. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. [pdf] [poster] Secured intranet portal for faculty, staff and students. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! with Yair Carmon, Kevin Tian and Aaron Sidford Verified email at stanford.edu - Homepage. I am 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 . DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . when do tulips bloom in maryland; indo pacific region upsc Navajo Math Circles Instructor. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Some I am still actively improving and all of them I am happy to continue polishing. with Aaron Sidford University, where CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. David P. Woodruff . Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. MS&E welcomes new faculty member, Aaron Sidford ! In International Conference on Machine Learning (ICML 2016). In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Articles 1-20. 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 . Try again later. ! We also provide two . Try again later. The authors of most papers are ordered alphabetically. We forward in this generation, Triumphantly. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs 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] [slides] theses are protected by copyright. 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. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. with Yair Carmon, Arun Jambulapati and Aaron Sidford ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford arXiv | conference pdf, Annie Marsden, Sergio Bacallado. I completed my PhD at Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Their, This "Cited by" count includes citations to the following articles in Scholar. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. /Length 11 0 R [pdf] [talk] SODA 2023: 4667-4767. Stanford University. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Here are some lecture notes that I have written over the years. 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. in math and computer science from Swarthmore College in 2008. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). the Operations Research group. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Vidya Muthukumar and Aaron Sidford There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Contact. [last name]@stanford.edu where [last name]=sidford. 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. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. I am broadly interested in optimization problems, sometimes in the intersection with machine learning Anup B. Rao. with Yair Carmon, Aaron Sidford and Kevin Tian 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. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). << Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. % Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . I am fortunate to be advised by Aaron Sidford. View Full Stanford Profile. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ 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. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Neural Information Processing Systems (NeurIPS), 2014. sidford@stanford.edu. stream However, many advances have come from a continuous viewpoint. Intranet Web Portal. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 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 . ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). with Yang P. Liu and Aaron Sidford. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Np%p `a!2D4! Alcatel flip phones are also ready to purchase with consumer cellular. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. From 2016 to 2018, I also worked in [pdf] [talk] Yair Carmon. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. publications by categories in reversed chronological order. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle 2013. Yujia Jin. The site facilitates research and collaboration in academic endeavors. . 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.
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