aaron sidford cvaaron sidford cv

with Aaron Sidford Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." with Arun Jambulapati, Aaron Sidford and Kevin Tian [pdf] [talk] [poster] My CV. Faculty Spotlight: Aaron Sidford. Improved Lower Bounds for Submodular Function Minimization. I enjoy understanding the theoretical ground of many algorithms that are Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. publications by categories in reversed chronological order. Their, This "Cited by" count includes citations to the following articles in Scholar. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss [pdf] [poster] ICML, 2016. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Efficient Convex Optimization Requires Superlinear Memory. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. 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. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. This site uses cookies from Google to deliver its services and to analyze traffic. [pdf] Here are some lecture notes that I have written over the years. . ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. 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 . ", "A short version of the conference publication under the same title. Before attending Stanford, I graduated from MIT in May 2018. /Filter /FlateDecode 2023. . Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. One research focus are dynamic algorithms (i.e. [pdf] [talk] [poster] ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. [pdf] [talk] [poster] Faculty and Staff Intranet. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Student Intranet. what is a blind trust for lottery winnings; ithaca college park school scholarships; Neural Information Processing Systems (NeurIPS), 2014. 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 . With Cameron Musco and Christopher Musco. in math and computer science from Swarthmore College in 2008. Personal Website. Yin Tat Lee and Aaron Sidford. [pdf] [talk] Before attending Stanford, I graduated from MIT in May 2018. 4 0 obj 2021. with Yair Carmon, Arun Jambulapati and Aaron Sidford Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA [pdf] Stanford, CA 94305 Faster energy maximization for faster maximum flow. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Two months later, he was found lying in a creek, dead from . 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 . ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Management Science & Engineering About Me. University of Cambridge MPhil. 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. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. From 2016 to 2018, I also worked in In International Conference on Machine Learning (ICML 2016). with Aaron Sidford I graduated with a PhD from Princeton University in 2018. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Stanford University. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Navajo Math Circles Instructor. 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 particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Source: appliancesonline.com.au. I often do not respond to emails about applications. Main Menu. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. SODA 2023: 5068-5089. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford rl1 I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Articles 1-20. 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. 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. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Selected for oral presentation. If you see any typos or issues, feel free to email me. Group Resources. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. O! 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 However, many advances have come from a continuous viewpoint. of practical importance. Before Stanford, I worked with John Lafferty at the University of Chicago. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. >> van vu professor, yale Verified email at yale.edu. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. << >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. 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. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan with Yair Carmon, Aaron Sidford and Kevin Tian By using this site, you agree to its use of cookies. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Title. 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 . 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 . University, where I am 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 . The authors of most papers are ordered alphabetically. 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. A nearly matching upper and lower bound for constant error here! Annie Marsden. In this talk, I will present a new algorithm for solving linear programs. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. [last name]@stanford.edu where [last name]=sidford. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Email / 4026. ", "Sample complexity for average-reward MDPs? 2016. resume/cv; publications. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. [pdf] sidford@stanford.edu. 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. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 /CreationDate (D:20230304061109-08'00') COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. My research is on the design and theoretical analysis of efficient algorithms and data structures.
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