Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Thesis, 2016. pdf. 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 . theory and graph applications. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Mail Code. We also provide two . Full CV is available here. Stanford University. Email: [name]@stanford.edu " 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. Goethe University in Frankfurt, Germany. 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 . "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Faculty Spotlight: Aaron Sidford. With Yair Carmon, John C. Duchi, and Oliver Hinder. aaron sidford cvnatural fibrin removalnatural fibrin removal I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Yujia Jin. I enjoy understanding the theoretical ground of many algorithms that are ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. SHUFE, where I was fortunate Aleksander Mdry; Generalized preconditioning and network flow problems to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration University, Research Institute for Interdisciplinary Sciences (RIIS) at Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in 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. [pdf] Many of my results use fast matrix multiplication (, 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 (. Navajo Math Circles Instructor. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. It was released on november 10, 2017. 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. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Yujia Jin. [pdf] [talk] [poster] . With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Our method improves upon the convergence rate of previous state-of-the-art linear programming . 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. 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. missouri noodling association president cnn. 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. In International Conference on Machine Learning (ICML 2016). 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. Source: www.ebay.ie Allen Liu. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 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. My research is on the design and theoretical analysis of efficient algorithms and data structures. 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. 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. Selected recent papers . Source: appliancesonline.com.au. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. arXiv preprint arXiv:2301.00457, 2023 arXiv. [last name]@stanford.edu where [last name]=sidford. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. ?_l) theses are protected by copyright. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. publications by categories in reversed chronological order. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Office: 380-T Nearly Optimal Communication and Query Complexity of Bipartite Matching . IEEE, 147-156. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization About Me. COLT, 2022. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! 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. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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. 4 0 obj Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Stanford University Roy Frostig, Sida Wang, Percy Liang, Chris Manning. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Stanford, CA 94305 We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Publications and Preprints. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ With Cameron Musco and Christopher Musco. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 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 AISTATS, 2021. Huang Engineering Center ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Secured intranet portal for faculty, staff and students. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. I was fortunate to work with Prof. Zhongzhi Zhang. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Follow. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Title. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Associate Professor of . Eigenvalues of the laplacian and their relationship to the connectedness of a graph. van vu professor, yale Verified email at yale.edu. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at 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. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). . 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