F+s9H [pdf] University, Research Institute for Interdisciplinary Sciences (RIIS) at with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian (, 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 (. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! AISTATS, 2021. Try again later. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. I am fortunate to be advised by Aaron Sidford. Yair Carmon. theory and graph applications. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Improves the stochas-tic convex optimization problem in parallel and DP setting. We forward in this generation, Triumphantly. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Publications and Preprints. theses are protected by copyright. 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. [pdf] We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. arXiv preprint arXiv:2301.00457, 2023 arXiv. rl1 [pdf] With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). 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. [pdf] 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. 9-21. I graduated with a PhD from Princeton University in 2018. Associate Professor of . David P. Woodruff . International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . 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. Source: www.ebay.ie Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. by Aaron Sidford. with Aaron Sidford The site facilitates research and collaboration in academic endeavors. 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. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. with Yair Carmon, Kevin Tian and Aaron Sidford D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. /CreationDate (D:20230304061109-08'00') This site uses cookies from Google to deliver its services and to analyze traffic. Applying this technique, we prove that any deterministic SFM algorithm . 2021 - 2022 Postdoc, Simons Institute & UC . About Me. aaron sidford cvis sea bass a bony fish to eat. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Anup B. Rao. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs CoRR abs/2101.05719 ( 2021 ) riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. 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. Before Stanford, I worked with John Lafferty at the University of Chicago. Goethe University in Frankfurt, Germany. 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. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. The design of algorithms is traditionally a discrete endeavor. in Mathematics and B.A. 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 [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. /Producer (Apache FOP Version 1.0) With Cameron Musco and Christopher Musco. with Yair Carmon, Arun Jambulapati and Aaron Sidford 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. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. We also provide two . Stanford University. 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. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization resume/cv; publications. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). in math and computer science from Swarthmore College in 2008. Two months later, he was found lying in a creek, dead from . . Google Scholar Digital Library; Russell Lyons and Yuval Peres. 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 . Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG SODA 2023: 4667-4767. ", Applied Math at Fudan Thesis, 2016. pdf. publications by categories in reversed chronological order. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. I was fortunate to work with Prof. Zhongzhi Zhang. Faster energy maximization for faster maximum flow. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in The authors of most papers are ordered alphabetically. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. 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 . Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Office: 380-T ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Aaron Sidford Stanford University Verified email at stanford.edu. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. in Chemistry at the University of Chicago. 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 Some I am still actively improving and all of them I am happy to continue polishing. In International Conference on Machine Learning (ICML 2016). sidford@stanford.edu. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games endobj I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. ICML, 2016. I am an Assistant Professor in the School of Computer Science at Georgia Tech. 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. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford 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. [pdf] [poster] 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. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. It was released on november 10, 2017. Secured intranet portal for faculty, staff and students. 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. Google Scholar; Probability on trees and . Best Paper Award. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. I also completed my undergraduate degree (in mathematics) at MIT. with Aaron Sidford With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. [pdf] [talk] My research focuses on AI and machine learning, with an emphasis on robotics applications. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games 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. I am broadly interested in mathematics and theoretical computer science. I often do not respond to emails about applications. Contact. ", "Sample complexity for average-reward MDPs? Unlike previous ADFOCS, this year the event will take place over the span of three weeks. University, where To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Stanford, CA 94305 The system can't perform the operation now. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Student Intranet. Stanford University MS&E welcomes new faculty member, Aaron Sidford ! Email / 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 .