Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. The design of algorithms is traditionally a discrete endeavor. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Two months later, he was found lying in a creek, dead from . ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). Goethe University in Frankfurt, Germany. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. KTH in Stockholm, Sweden, and my BSc + MSc at the 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. Aaron Sidford | Management Science and Engineering << This site uses cookies from Google to deliver its services and to analyze traffic. Aaron Sidford Mary Wootters - Google Aaron Sidford - Teaching Aaron Sidford | Stanford Online Aaron Sidford - All Publications Another research focus are optimization algorithms. UGTCS Faster Matroid Intersection Princeton University to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration 2016. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. 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. IEEE, 147-156. The system can't perform the operation now. [pdf] Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 1 -Regression in Nearly Linear Time for Dense Instances. 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. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Personal Website. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. 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 fortunate to be advised by Aaron Sidford. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods pdf, Sequential Matrix Completion. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. 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. Contact. Articles Cited by Public access. ! Selected for oral presentation. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Aaron Sidford - My Group Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. I am broadly interested in mathematics and theoretical computer science. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Advanced Data Structures (6.851) - Massachusetts Institute of Technology theses are protected by copyright. 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. Sampling random spanning trees faster than matrix multiplication My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 22nd Max Planck Advanced Course on the Foundations of Computer Science We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Before attending Stanford, I graduated from MIT in May 2018. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Done under the mentorship of M. Malliaris. ", "A short version of the conference publication under the same title. aaron sidford cv July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. . O! Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. 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 . Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA My long term goal is to bring robots into human-centered domains such as homes and hospitals. . Simple MAP inference via low-rank relaxations. Lower Bounds for Finding Stationary Points II: First-Order Methods Conference on Learning Theory (COLT), 2015. SODA 2023: 5068-5089. /CreationDate (D:20230304061109-08'00') 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. 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. Improved Lower Bounds for Submodular Function Minimization Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Faculty and Staff Intranet. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. F+s9H Np%p `a!2D4! Adam Bouland - Stanford University International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods "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. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. ICML, 2016. Anup B. Rao - Google Scholar Lower bounds for finding stationary points II: first-order methods. 4026. Improves the stochas-tic convex optimization problem in parallel and DP setting. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . 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. Annie Marsden. The following articles are merged in Scholar. Office: 380-T with Aaron Sidford Their, This "Cited by" count includes citations to the following articles in Scholar. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Aviv Tamar - Reinforcement Learning Research Labs - Technion MS&E213 / CS 269O - Introduction to Optimization Theory MS&E welcomes new faculty member, Aaron Sidford ! I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. with Aaron Sidford Yujia Jin. how . van vu professor, yale Verified email at yale.edu. 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). SHUFE, where I was fortunate They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 5 0 obj I also completed my undergraduate degree (in mathematics) at MIT. I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Email: sidford@stanford.edu. In this talk, I will present a new algorithm for solving linear programs. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. CV (last updated 01-2022): PDF Contact. aaron sidford cv Etude for the Park City Math Institute Undergraduate Summer School. 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] Associate Professor of . % 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. Sequential Matrix Completion. My CV. Aaron Sidford receives best paper award at COLT 2022 SODA 2023: 4667-4767. Sivakanth Gopi at Microsoft Research Aleksander Mdry; Generalized preconditioning and network flow problems Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . ", Applied Math at Fudan endobj This is the academic homepage of Yang Liu (I publish under Yang P. Liu). /Creator (Apache FOP Version 1.0) Title. 2021 - 2022 Postdoc, Simons Institute & UC . Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. missouri noodling association president cnn. 2016. 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, " 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. (ACM Doctoral Dissertation Award, Honorable Mention.) 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 . in Mathematics and B.A. I am an Assistant Professor in the School of Computer Science at Georgia Tech. Yin Tat Lee and Aaron Sidford. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. 2023. . I graduated with a PhD from Princeton University in 2018. Yang P. Liu - GitHub Pages theory and graph applications. A nearly matching upper and lower bound for constant error here! 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. In each setting we provide faster exact and approximate algorithms. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana.
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