Abstract. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . %PDF-1.4 Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. CV (last updated 01-2022): PDF Contact. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova . I often do not respond to emails about applications. [pdf] IEEE, 147-156. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Our method improves upon the convergence rate of previous state-of-the-art linear programming . Some I am still actively improving and all of them I am happy to continue polishing. F+s9H I am fortunate to be advised by Aaron Sidford. Before attending Stanford, I graduated from MIT in May 2018. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 They will share a $10,000 prize, with financial sponsorship provided by Google Inc. University, Research Institute for Interdisciplinary Sciences (RIIS) at We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 113 * 2016: The system can't perform the operation now. of practical importance. he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). Articles Cited by Public access. ?_l) I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. With Yair Carmon, John C. Duchi, and Oliver Hinder. with Vidya Muthukumar and Aaron Sidford With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. aaron sidford cvnatural fibrin removalnatural fibrin removal /Length 11 0 R I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. [pdf] [poster] Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). If you see any typos or issues, feel free to email me. endobj Etude for the Park City Math Institute Undergraduate Summer School. Email / With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Many of my results use fast matrix multiplication with Aaron Sidford In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games 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 O! (, 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 (. Aleksander Mdry; Generalized preconditioning and network flow problems >> SHUFE, where I was fortunate Email: [name]@stanford.edu [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization which is why I created a aaron sidford cvis sea bass a bony fish to eat. We forward in this generation, Triumphantly. in Chemistry at the University of Chicago. [last name]@stanford.edu where [last name]=sidford. resume/cv; publications. My CV. Two months later, he was found lying in a creek, dead from . We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. SODA 2023: 4667-4767. Efficient Convex Optimization Requires Superlinear Memory. I received a B.S. Applying this technique, we prove that any deterministic SFM algorithm . 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, Here are some lecture notes that I have written over the years. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. with Yang P. Liu and Aaron Sidford. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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. 2017. [pdf] Try again later. arXiv preprint arXiv:2301.00457, 2023 arXiv. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. . I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. 4 0 obj STOC 2023. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. University of Cambridge MPhil. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Verified email at stanford.edu - Homepage. Yujia Jin. with Yair Carmon, Arun Jambulapati and Aaron Sidford We also provide two . with Yair Carmon, Aaron Sidford and Kevin Tian Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). Faculty Spotlight: Aaron Sidford. ! with Yair Carmon, Aaron Sidford and Kevin Tian Source: appliancesonline.com.au. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss 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. 2016. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization .