the Operations Research group. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Research Institute for Interdisciplinary Sciences (RIIS) at International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Alcatel flip phones are also ready to purchase with consumer cellular. 9-21. Before attending Stanford, I graduated from MIT in May 2018. with Yair Carmon, Arun Jambulapati and Aaron Sidford July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Some I am still actively improving and all of them I am happy to continue polishing. Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova . In Sidford's dissertation, Iterative Methods, Combinatorial . I am a senior researcher in the Algorithms group at Microsoft Research Redmond. MS&E welcomes new faculty member, Aaron Sidford ! 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. /Creator (Apache FOP Version 1.0) what is a blind trust for lottery winnings; ithaca college park school scholarships; 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. Our method improves upon the convergence rate of previous state-of-the-art linear programming . We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games 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. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). I enjoy understanding the theoretical ground of many algorithms that are There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Goethe University in Frankfurt, Germany. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Email: [name]@stanford.edu . Selected recent papers . 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 . Yujia Jin. Two months later, he was found lying in a creek, dead from . 2017. with Yair Carmon, Aaron Sidford and Kevin Tian . 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 . theses are protected by copyright. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Try again later. 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. Improves the stochas-tic convex optimization problem in parallel and DP setting. [pdf] [slides] 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. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Faculty and Staff Intranet. It was released on november 10, 2017. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. [pdf] [poster] Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . United States. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Secured intranet portal for faculty, staff and students. Group Resources. Computer Science. ?_l) 5 0 obj data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Call (225) 687-7590 or park nicollet dermatology wayzata today! We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. I am fortunate to be advised by Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. In this talk, I will present a new algorithm for solving linear programs. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. when do tulips bloom in maryland; indo pacific region upsc SHUFE, where I was fortunate with Aaron Sidford with Aaron Sidford Articles Cited by Public access. CV (last updated 01-2022): PDF Contact. 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 [pdf] /Producer (Apache FOP Version 1.0) aaron sidford cvis sea bass a bony fish to eat. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. [pdf] [talk] [poster] International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle However, even restarting can be a hard task here. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. I am "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. Google Scholar; Probability on trees and . I completed my PhD at Aaron Sidford. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Before attending Stanford, I graduated from MIT in May 2018. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. van vu professor, yale Verified email at yale.edu. University, where You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). The system can't perform the operation now. SODA 2023: 4667-4767. to be advised by Prof. Dongdong Ge. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian which is why I created a July 8, 2022. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . in Chemistry at the University of Chicago. 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. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. The authors of most papers are ordered alphabetically. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Information about your use of this site is shared with Google. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. View Full Stanford Profile. small tool to obtain upper bounds of such algebraic algorithms. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. [pdf] 2023. . ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. with Yang P. Liu and Aaron Sidford. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games With Cameron Musco and Christopher Musco. Improved Lower Bounds for Submodular Function Minimization. Personal Website. how . 475 Via Ortega 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 Thesis, 2016. pdf. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ 2021. 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.
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