ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Yin Tat Lee and Aaron Sidford. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& From 2016 to 2018, I also worked in >> NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Abstract. 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. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. In each setting we provide faster exact and approximate algorithms. View Full Stanford Profile. I am broadly interested in mathematics and theoretical computer science. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). [pdf] Associate Professor of . Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Google Scholar; Probability on trees and . with Aaron Sidford (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. /Length 11 0 R I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Management Science & Engineering With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. ReSQueing Parallel and Private Stochastic Convex Optimization. BayLearn, 2021, 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. 2016. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Follow. pdf, Sequential Matrix Completion. I regularly advise Stanford students from a variety of departments. 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. 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. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. I am fortunate to be advised by Aaron Sidford . Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. what is a blind trust for lottery winnings; ithaca college park school scholarships; I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent with Vidya Muthukumar and Aaron Sidford Another research focus are optimization algorithms. SHUFE, where I was fortunate . Personal Website. [pdf] [talk] [poster] I received a B.S. 2023. . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration . 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 . July 8, 2022. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Improved Lower Bounds for Submodular Function Minimization. 4 0 obj 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 Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Before attending Stanford, I graduated from MIT in May 2018. Our method improves upon the convergence rate of previous state-of-the-art linear programming . [pdf] Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. theory and graph applications. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. >> In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). 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 . 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. Google Scholar Digital Library; Russell Lyons and Yuval Peres. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 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. rl1 Aleksander Mdry; Generalized preconditioning and network flow problems 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. [pdf] [talk] [poster] Before attending Stanford, I graduated from MIT in May 2018. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. If you see any typos or issues, feel free to email me. Selected recent papers . with Aaron Sidford /CreationDate (D:20230304061109-08'00') 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. With Cameron Musco and Christopher Musco. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. with Arun Jambulapati, Aaron Sidford and Kevin Tian with Kevin Tian and Aaron Sidford Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. With Yair Carmon, John C. Duchi, and Oliver Hinder. 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. 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. 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). 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. However, many advances have come from a continuous viewpoint. Annie Marsden. missouri noodling association president cnn. Stanford, CA 94305 ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Try again later. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Group Resources. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. by Aaron Sidford. Conference on Learning Theory (COLT), 2015. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." 2016. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! 4026. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Roy Frostig, Sida Wang, Percy Liang, Chris Manning. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. The following articles are merged in Scholar. to be advised by Prof. Dongdong Ge. << [pdf] [talk] [poster] STOC 2023. I am broadly interested in mathematics and theoretical computer science. Faster energy maximization for faster maximum flow. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. University, where Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. van vu professor, yale Verified email at yale.edu. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . I often do not respond to emails about applications. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Source: appliancesonline.com.au. COLT, 2022. Email / International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods /N 3 I completed my PhD at ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! [pdf] [poster] ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Stanford University. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. 2013. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Here is a slightly more formal third-person biography, and here is a recent-ish CV. in Mathematics and B.A. Efficient Convex Optimization Requires Superlinear Memory. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper how . 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. [pdf] [talk] Summer 2022: I am currently a research scientist intern at DeepMind in London. Allen Liu. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. [pdf] [poster] Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. University of Cambridge MPhil. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. David P. Woodruff . O! Research Institute for Interdisciplinary Sciences (RIIS) at I was fortunate to work with Prof. Zhongzhi Zhang. 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 riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Some I am still actively improving and all of them I am happy to continue polishing. " 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. Algorithms Optimization and Numerical Analysis. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. MS&E welcomes new faculty member, Aaron Sidford ! arXiv preprint arXiv:2301.00457, 2023 arXiv. when do tulips bloom in maryland; indo pacific region upsc ?_l) Information about your use of this site is shared with Google. (, 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 (. However, even restarting can be a hard task here. 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. 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 .
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