Many of my results use fast matrix multiplication Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Faster energy maximization for faster maximum flow. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). By using this site, you agree to its use of cookies. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Algorithms Optimization and Numerical Analysis. Aaron's research interests lie in optimization, the theory of computation, and the . Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. [pdf] [talk] [poster] Personal Website. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 4 0 obj Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. The following articles are merged in Scholar. Yin Tat Lee and Aaron Sidford. AISTATS, 2021. 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. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Etude for the Park City Math Institute Undergraduate Summer School. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Done under the mentorship of M. Malliaris. We forward in this generation, Triumphantly. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Yujia Jin. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries 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. with Yair Carmon, Arun Jambulapati and Aaron Sidford Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Mail Code. [pdf] [poster] Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) If you see any typos or issues, feel free to email me. Two months later, he was found lying in a creek, dead from . 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. rl1 9-21. O! I often do not respond to emails about applications. [pdf] Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Faculty Spotlight: Aaron Sidford. 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. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. to be advised by Prof. Dongdong Ge. sidford@stanford.edu. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [pdf] [slides] with Arun Jambulapati, Aaron Sidford and Kevin Tian . ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Allen Liu. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Office: 380-T ! Secured intranet portal for faculty, staff and students. Email / The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Aaron Sidford. Thesis, 2016. pdf. Research Institute for Interdisciplinary Sciences (RIIS) at 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. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. View Full Stanford Profile. University of Cambridge MPhil. Links. 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. Enrichment of Network Diagrams for Potential Surfaces. One research focus are dynamic algorithms (i.e. Best Paper Award. ?_l) Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent 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. Sequential Matrix Completion. 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. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. aaron sidford cvis sea bass a bony fish to eat. in math and computer science from Swarthmore College in 2008. My long term goal is to bring robots into human-centered domains such as homes and hospitals. resume/cv; publications. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. I am broadly interested in mathematics and theoretical computer science. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. In International Conference on Machine Learning (ICML 2016). 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. Publications and Preprints. Simple MAP inference via low-rank relaxations. The authors of most papers are ordered alphabetically. in Chemistry at the University of Chicago. Yang P. Liu, Aaron Sidford, Department of Mathematics % 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. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). CV (last updated 01-2022): PDF Contact. Information about your use of this site is shared with Google. Semantic parsing on Freebase from question-answer pairs. 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. Follow. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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 . 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. Aaron Sidford Stanford University Verified email at stanford.edu. Honorable Mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Secured intranet portal for faculty, staff and students. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." I received a B.S. Some I am still actively improving and all of them I am happy to continue polishing. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. In this talk, I will present a new algorithm for solving linear programs. Source: www.ebay.ie Group Resources. Abstract. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 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. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). of practical importance. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . 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. I am broadly interested in optimization problems, sometimes in the intersection with machine learning We also provide two . Before Stanford, I worked with John Lafferty at the University of Chicago. with Yair Carmon, Arun Jambulapati and Aaron Sidford I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs [pdf] [poster] Email: sidford@stanford.edu. arXiv preprint arXiv:2301.00457, 2023 arXiv. Assistant Professor of Management Science and Engineering and of Computer Science. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). van vu professor, yale Verified email at yale.edu. 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. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Before attending Stanford, I graduated from MIT in May 2018. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . . Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. 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. Their, This "Cited by" count includes citations to the following articles in Scholar. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Verified email at stanford.edu - Homepage. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. KTH in Stockholm, Sweden, and my BSc + MSc at the Slides from my talk at ITCS. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. 4026. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. >> In Sidford's dissertation, Iterative Methods, Combinatorial . with Aaron Sidford to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching The system can't perform the operation now. 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. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . My research is on the design and theoretical analysis of efficient algorithms and data structures. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Title. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. AISTATS, 2021. University, where Alcatel flip phones are also ready to purchase with consumer cellular. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration This is the academic homepage of Yang Liu (I publish under Yang P. Liu). with Yang P. Liu and Aaron Sidford. 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. 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 With Yair Carmon, John C. Duchi, and Oliver Hinder. %PDF-1.4 With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. 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 particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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. About Me. . Try again later. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). SHUFE, where I was fortunate Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . stream STOC 2023. 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 {{{;}#q8?\. 2016. >> I enjoy understanding the theoretical ground of many algorithms that are I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. However, many advances have come from a continuous viewpoint. with Aaron Sidford She was 19 years old and looking forward to the start of classes and reuniting with her college pals. With Cameron Musco and Christopher Musco. Summer 2022: I am currently a research scientist intern at DeepMind in London. "t a","H I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. 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. ", "Team-convex-optimization for solving discounted and average-reward MDPs! This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Stanford, CA 94305 Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA However, even restarting can be a hard task here. 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 . Goethe University in Frankfurt, Germany. Stanford University. Nearly Optimal Communication and Query Complexity of Bipartite Matching . With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Articles 1-20. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods I was fortunate to work with Prof. Zhongzhi Zhang. Aleksander Mdry; Generalized preconditioning and network flow problems SODA 2023: 5068-5089. I am fortunate to be advised by Aaron Sidford . (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. with Kevin Tian and Aaron Sidford Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. /Producer (Apache FOP Version 1.0) In each setting we provide faster exact and approximate algorithms. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. 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 . in Mathematics and B.A. 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 . 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.