Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Slides from my talk at ITCS. The site facilitates research and collaboration in academic endeavors. KTH in Stockholm, Sweden, and my BSc + MSc at the 2016. Some I am still actively improving and all of them I am happy to continue polishing. CoRR abs/2101.05719 ( 2021 ) in math and computer science from Swarthmore College in 2008. It was released on november 10, 2017. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. 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. Goethe University in Frankfurt, Germany. 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. Student Intranet. [pdf] [talk] ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Instructor: Aaron Sidford Winter 2018 Time: Tuesdays and Thursdays, 10:30 AM - 11:50 AM Room: Education Building, Room 128 Here is the course syllabus. sidford@stanford.edu. 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. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper STOC 2023. ", Applied Math at Fudan In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . with Aaron Sidford . I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Improved Lower Bounds for Submodular Function Minimization. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I also completed my undergraduate degree (in mathematics) at MIT. [last name]@stanford.edu where [last name]=sidford. Huang Engineering Center Aaron Sidford. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games with Kevin Tian and Aaron Sidford 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. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Before attending Stanford, I graduated from MIT in May 2018. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. I was fortunate to work with Prof. Zhongzhi Zhang. Email: sidford@stanford.edu. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. [pdf] [talk] [poster] 2017. ICML, 2016. ?_l) [pdf] ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. We also provide two . Source: appliancesonline.com.au. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. with Yair Carmon, Aaron Sidford and Kevin Tian . 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 My interests are in the intersection of algorithms, statistics, optimization, and machine learning. [pdf] July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. with Aaron Sidford arXiv preprint arXiv:2301.00457, 2023 arXiv. 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 . Main Menu. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Aaron Sidford Stanford University Verified email at stanford.edu. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) in Mathematics and B.A. in Chemistry at the University of Chicago. of practical importance. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 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 Assistant Professor of Management Science and Engineering and of Computer Science. About Me. University of Cambridge MPhil. missouri noodling association president cnn. Full CV is available here. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 2021 - 2022 Postdoc, Simons Institute & UC . Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. with Aaron Sidford [pdf] [slides] In International Conference on Machine Learning (ICML 2016). I often do not respond to emails about applications. July 8, 2022. Algorithms Optimization and Numerical Analysis. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration However, many advances have come from a continuous viewpoint. 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. In each setting we provide faster exact and approximate algorithms. /CreationDate (D:20230304061109-08'00') Yin Tat Lee and Aaron Sidford. with Vidya Muthukumar and Aaron Sidford Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. In submission. SHUFE, where I was fortunate Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. If you see any typos or issues, feel free to email me. "t a","H Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y In this talk, I will present a new algorithm for solving linear programs. to be advised by Prof. Dongdong Ge. 5 0 obj << Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! I am broadly interested in mathematics and theoretical computer science. Applying this technique, we prove that any deterministic SFM algorithm . ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! 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. [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. 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. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. The following articles are merged in Scholar. when do tulips bloom in maryland; indo pacific region upsc Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! I completed my PhD at 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. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Secured intranet portal for faculty, staff and students. "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. Group Resources. Email / 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. With Yair Carmon, John C. Duchi, and Oliver Hinder. 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. 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. Articles 1-20. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Our method improves upon the convergence rate of previous state-of-the-art linear programming . [pdf] O! 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. A nearly matching upper and lower bound for constant error here! /N 3 Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. The authors of most papers are ordered alphabetically. Links. I graduated with a PhD from Princeton University in 2018. [pdf] In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. In Sidford's dissertation, Iterative Methods, Combinatorial . Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Np%p `a!2D4! MS&E welcomes new faculty member, Aaron Sidford ! I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. ", "Team-convex-optimization for solving discounted and average-reward MDPs! ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Allen Liu. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. With Cameron Musco and Christopher Musco. 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. [pdf] [talk] [poster] theses are protected by copyright. I am broadly interested in mathematics and theoretical computer science. publications by categories in reversed chronological order. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 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. University, where (, 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 (. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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. >> My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Simple MAP inference via low-rank relaxations. Another research focus are optimization algorithms. Try again later. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Intranet Web Portal. Associate Professor of . The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Aleksander Mdry; Generalized preconditioning and network flow problems Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. I received a B.S. One research focus are dynamic algorithms (i.e. by Aaron Sidford. 475 Via Ortega . Journal of Machine Learning Research, 2017 (arXiv). with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Yang P. Liu, Aaron Sidford, Department of Mathematics The system can't perform the operation now. 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. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. /Producer (Apache FOP Version 1.0) We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. stream >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. [pdf] [poster] With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. 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. 2013. Conference on Learning Theory (COLT), 2015. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Contact. 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. [pdf] [poster] They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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. ReSQueing Parallel and Private Stochastic Convex Optimization. COLT, 2022. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. aaron sidford cvis sea bass a bony fish to eat. 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 Yair Carmon, Arun Jambulapati and Aaron Sidford 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, 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. what is a blind trust for lottery winnings; ithaca college park school scholarships; [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. SODA 2023: 5068-5089.

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