Aaron Sidford Stanford University Verified email at stanford.edu. Aaron Sidford's Homepage - Stanford University Intranet Web Portal. Yang P. Liu - GitHub Pages MS&E welcomes new faculty member, Aaron Sidford ! Faster energy maximization for faster maximum flow. 2013. 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 . Aaron Sidford - Selected Publications CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Google Scholar; Probability on trees and . Neural Information Processing Systems (NeurIPS), 2014. 2015 Doctoral Dissertation Award - Association for Computing Machinery aaron sidford cv Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss 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. 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 Yang P. Liu and Aaron Sidford. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Interior Point Methods for Nearly Linear Time Algorithms | ISL Microsoft Research Faculty Fellowship 2020: Researchers in academia at Publications | Jakub Pachocki - Harvard University In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Aaron Sidford xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. dblp: Daogao Liu 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. O! CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat 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 My research focuses on AI and machine learning, with an emphasis on robotics applications. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). I regularly advise Stanford students from a variety of departments. aaron sidford cv natural fibrin removal - libiot.kku.ac.th aaron sidford cvis sea bass a bony fish to eat. in Chemistry at the University of Chicago. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). The system can't perform the operation now. Adam Bouland - Stanford University Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Journal of Machine Learning Research, 2017 (arXiv). In International Conference on Machine Learning (ICML 2016). with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Accelerated Methods for NonConvex Optimization | Semantic Scholar Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Aviv Tamar - Reinforcement Learning Research Labs - Technion with Aaron Sidford data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. July 8, 2022. IEEE, 147-156. 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. She was 19 years old and looking - freewareppc.com With Cameron Musco and Christopher Musco. "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. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Stanford University 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. Yang P. Liu, Aaron Sidford, Department of Mathematics We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Assistant Professor of Management Science and Engineering and of Computer Science. /Length 11 0 R ", "A short version of the conference publication under the same title. with Yair Carmon, Aaron Sidford and Kevin Tian ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. which is why I created a If you see any typos or issues, feel free to email me. Goethe University in Frankfurt, Germany. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. I was fortunate to work with Prof. Zhongzhi Zhang. Aaron Sidford (sidford@stanford.edu) 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. Group Resources. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Source: www.ebay.ie Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Iterative methods, combinatorial optimization, and linear programming She was 19 years old and looking forward to the start of classes and reuniting with her college pals. 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. 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. Research Institute for Interdisciplinary Sciences (RIIS) at Try again later. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Selected recent papers . Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. with Yair Carmon, Arun Jambulapati and Aaron Sidford 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. 2023. . Faster Matroid Intersection Princeton University 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, Allen Liu - GitHub Pages Improved Lower Bounds for Submodular Function Minimization. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ Faculty Spotlight: Aaron Sidford - Management Science and Engineering missouri noodling association president cnn. Cameron Musco - Manning College of Information & Computer Sciences [pdf] [talk] 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 . I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. . I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. . This is the academic homepage of Yang Liu (I publish under Yang P. 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. In submission. If you see any typos or issues, feel free to email me. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. << 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. Aaron Sidford - Google Scholar I am a senior researcher in the Algorithms group at Microsoft Research Redmond. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Associate Professor of . . SODA 2023: 5068-5089. [pdf] [poster] CV (last updated 01-2022): PDF Contact. Sequential Matrix Completion. We forward in this generation, Triumphantly. [pdf] to be advised by Prof. Dongdong Ge. I received a B.S. 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 . arXiv preprint arXiv:2301.00457, 2023 arXiv. 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 We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." how . In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. David P. Woodruff - Carnegie Mellon University 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 . Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. MS&E213 / CS 269O - Introduction to Optimization Theory 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. SODA 2023: 4667-4767. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Source: appliancesonline.com.au. small tool to obtain upper bounds of such algebraic algorithms. Email: sidford@stanford.edu. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 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. I often do not respond to emails about applications. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games van vu professor, yale Verified email at yale.edu. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . [pdf] [poster] To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. I am broadly interested in optimization problems, sometimes in the intersection with machine learning /Filter /FlateDecode ! /Producer (Apache FOP Version 1.0) % By using this site, you agree to its use of cookies. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Aaron Sidford. 4 0 obj ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. University, Research Institute for Interdisciplinary Sciences (RIIS) at . The following articles are merged in Scholar. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Personal Website. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . I am fortunate to be advised by Aaron Sidford. Vatsal Sharan - GitHub Pages July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. 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. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Office: 380-T With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Follow. Verified email at stanford.edu - Homepage. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. 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 with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games 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. Etude for the Park City Math Institute Undergraduate Summer School. with Yair Carmon, Kevin Tian and Aaron Sidford I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Yujia Jin - Stanford University I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. CoRR abs/2101.05719 ( 2021 ) NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. << [pdf] [poster] 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! in math and computer science from Swarthmore College in 2008. In each setting we provide faster exact and approximate algorithms. 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. One research focus are dynamic algorithms (i.e. I am Publications | Salil Vadhan Roy Frostig - Stanford University sidford@stanford.edu. In Sidford's dissertation, Iterative Methods, Combinatorial . Annie Marsden. Sampling random spanning trees faster than matrix multiplication However, many advances have come from a continuous viewpoint. 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. 9-21. Before Stanford, I worked with John Lafferty at the University of Chicago. aaron sidford cv ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). of practical importance. 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. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space The design of algorithms is traditionally a discrete endeavor.
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