pdf, Sequential Matrix Completion. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. MS&E welcomes new faculty member, Aaron Sidford ! the Operations Research group. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. . Secured intranet portal for faculty, staff and students. 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). with Yair Carmon, Aaron Sidford and Kevin Tian Many of my results use fast matrix multiplication missouri noodling association president cnn. >> rl1 theory and graph applications. [pdf] Simple MAP inference via low-rank relaxations. STOC 2023. Neural Information Processing Systems (NeurIPS), 2014. Full CV is available here. Efficient Convex Optimization Requires Superlinear Memory. by Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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, 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. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Aleksander Mdry; Generalized preconditioning and network flow problems My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). >> Email: [name]@stanford.edu with Yair Carmon, Kevin Tian and Aaron Sidford In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Aaron Sidford. I enjoy understanding the theoretical ground of many algorithms that are It was released on november 10, 2017. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. United States. [pdf] ICML, 2016. Here are some lecture notes that I have written over the years. 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 . arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 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. 2021 - 2022 Postdoc, Simons Institute & UC . [pdf] [talk] Applying this technique, we prove that any deterministic SFM algorithm . Secured intranet portal for faculty, staff and students. SHUFE, where I was fortunate Aaron Sidford Stanford University Verified email at stanford.edu. Alcatel flip phones are also ready to purchase with consumer cellular. Articles 1-20. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian I received a B.S. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. 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. Navajo Math Circles Instructor. . 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. with Arun Jambulapati, Aaron Sidford and Kevin Tian CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. ReSQueing Parallel and Private Stochastic Convex Optimization. Nearly Optimal Communication and Query Complexity of Bipartite Matching . We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 113 * 2016: The system can't perform the operation now. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . with Yair Carmon, Aaron Sidford and Kevin Tian [pdf] [poster] We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Try again later. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. stream By using this site, you agree to its use of cookies. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. 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. 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. << I am broadly interested in optimization problems, sometimes in the intersection with machine learning Conference on Learning Theory (COLT), 2015. F+s9H ", "Team-convex-optimization for solving discounted and average-reward MDPs! van vu professor, yale Verified email at yale.edu. Title. Google Scholar Digital Library; Russell Lyons and Yuval Peres. with Yair Carmon, Arun Jambulapati and Aaron Sidford . In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. 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. View Full Stanford Profile. Before Stanford, I worked with John Lafferty at the University of Chicago. We also provide two . [pdf] [poster] 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 present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . in math and computer science from Swarthmore College in 2008. with Kevin Tian and Aaron Sidford Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Try again later. A nearly matching upper and lower bound for constant error here! [pdf] (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. I regularly advise Stanford students from a variety of departments. My CV. 5 0 obj D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. I am broadly interested in mathematics and theoretical computer science. 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 . My research is on the design and theoretical analysis of efficient algorithms and data structures. COLT, 2022. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Contact. 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. In Sidford's dissertation, Iterative Methods, Combinatorial . 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Research Institute for Interdisciplinary Sciences (RIIS) at My long term goal is to bring robots into human-centered domains such as homes and hospitals. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. 4 0 obj My research focuses on AI and machine learning, with an emphasis on robotics applications. Information about your use of this site is shared with Google. 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. Sequential Matrix Completion. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Abstract. Aaron Sidford's 143 research works with 2,861 citations and 1,915 reads, including: Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Their, This "Cited by" count includes citations to the following articles in Scholar. 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. Personal Website. Office: 380-T Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. 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 . 9-21. We forward in this generation, Triumphantly. Associate Professor of . Group Resources. 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 We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Here are some lecture notes that I have written over the years. The design of algorithms is traditionally a discrete endeavor. when do tulips bloom in maryland; indo pacific region upsc publications by categories in reversed chronological order. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . with Aaron Sidford With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games ! 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. Stanford University. 2016. Goethe University in Frankfurt, Germany. The system can't perform the operation now. Management Science & Engineering Stanford, CA 94305 Faster energy maximization for faster maximum flow. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. 2016. 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. CoRR abs/2101.05719 ( 2021 ) Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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 . Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Source: appliancesonline.com.au. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. 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. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Links. 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. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University I am broadly interested in mathematics and theoretical computer science. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Annie Marsden. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. If you see any typos or issues, feel free to email me. Another research focus are optimization algorithms. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Here is a slightly more formal third-person biography, and here is a recent-ish CV. 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 . International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Yair Carmon. SODA 2023: 5068-5089. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. endobj One research focus are dynamic algorithms (i.e. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. 4026. Thesis, 2016. pdf. Etude for the Park City Math Institute Undergraduate Summer School. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford.
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