aaron sidford cv

Mail Code. pdf, Sequential Matrix Completion. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Before attending Stanford, I graduated from MIT in May 2018. I am fortunate to be advised by Aaron Sidford . resume/cv; publications. [pdf] Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. 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. SODA 2023: 4667-4767. It was released on november 10, 2017. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 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 Yujia Jin. [pdf] The following articles are merged in Scholar. 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. Stanford University. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Yair Carmon, Kevin Tian and Aaron Sidford We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University [pdf] [talk] [poster] Full CV is available here. 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. the Operations Research group. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. stream The system can't perform the operation now. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. I am fortunate to be advised by Aaron Sidford. Try again later. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford I am broadly interested in optimization problems, sometimes in the intersection with machine learning Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Management Science & Engineering Associate Professor of . [pdf] [poster] About Me. I am broadly interested in mathematics and theoretical computer science. Google Scholar; Probability on trees and . The site facilitates research and collaboration in academic endeavors. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Annie Marsden. Secured intranet portal for faculty, staff and students. which is why I created a I regularly advise Stanford students from a variety of departments. with Yang P. Liu and Aaron Sidford. Another research focus are optimization algorithms. with Aaron Sidford Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games with Aaron Sidford I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. SODA 2023: 5068-5089. View Full Stanford Profile. Some I am still actively improving and all of them I am happy to continue polishing. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Journal of Machine Learning Research, 2017 (arXiv). 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, Email: sidford@stanford.edu. 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. 2016. Efficient Convex Optimization Requires Superlinear Memory. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Research Institute for Interdisciplinary Sciences (RIIS) at ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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 Np%p `a!2D4! theory and graph applications. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Aaron Sidford. This site uses cookies from Google to deliver its services and to analyze traffic. In this talk, I will present a new algorithm for solving linear programs. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). with Aaron Sidford To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Verified email at stanford.edu - Homepage. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Stanford, CA 94305 I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 113 * 2016: The system can't perform the operation now. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs ", "Sample complexity for average-reward MDPs? >> /Producer (Apache FOP Version 1.0) "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. . sidford@stanford.edu. what is a blind trust for lottery winnings; ithaca college park school scholarships; Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . In International Conference on Machine Learning (ICML 2016). 4 0 obj how . Main Menu. Two months later, he was found lying in a creek, dead from . endobj ICML, 2016. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 4026. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. theses are protected by copyright. Computer Science. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. [pdf] [last name]@stanford.edu where [last name]=sidford. . February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. ", "A short version of the conference publication under the same title. Yujia Jin. AISTATS, 2021. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . 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. F+s9H Links. when do tulips bloom in maryland; indo pacific region upsc I also completed my undergraduate degree (in mathematics) at MIT. Etude for the Park City Math Institute Undergraduate Summer School. Articles 1-20. My CV. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent KTH in Stockholm, Sweden, and my BSc + MSc at the Selected for oral presentation. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Our method improves upon the convergence rate of previous state-of-the-art linear programming . By using this site, you agree to its use of cookies. Secured intranet portal for faculty, staff and students. I graduated with a PhD from Princeton University in 2018. 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 . Contact. Algorithms Optimization and Numerical Analysis. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG with Yair Carmon, Aaron Sidford and Kevin Tian ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Sequential Matrix Completion. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. with Kevin Tian and Aaron Sidford 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. 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. [pdf] [poster] 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. Conference on Learning Theory (COLT), 2015. 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. 2021 - 2022 Postdoc, Simons Institute & UC . Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. In submission. Personal Website. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. O! /Length 11 0 R ! by Aaron Sidford. missouri noodling association president cnn. In Sidford's dissertation, Iterative Methods, Combinatorial . I am Slides from my talk at ITCS. STOC 2023. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Email: [name]@stanford.edu ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Yin Tat Lee and Aaron Sidford. 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. with Aaron Sidford I was fortunate to work with Prof. Zhongzhi Zhang. Source: www.ebay.ie The authors of most papers are ordered alphabetically. (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, In Symposium on Foundations of Computer Science (FOCS 2015), Machtey Award for Best Student Paper (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv), Competing with the Empirical Risk Minimizer in a Single Pass, With Roy Frostig, Rong Ge, and Sham Kakade, In Conference on Learning Theory (COLT 2015) (arXiv), Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, In International Conference on Machine Learning (ICML 2015) (arXiv), Uniform Sampling for Matrix Approximation, With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng, In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2014), Best Paper Award and Machtey Award for Best Student Paper (arXiv), Single Pass Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco, An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations, With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia, In Symposium on Discrete Algorithms (SODA 2014), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv), A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time, With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu, In Symposium on the Theory of Computing (STOC 2013) (arXiv), SIAM Journal on Computing (arXiv before merge), Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space, With Jack Murtagh, Omer Reingold, and Salil Vadhan, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods.

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