My long term goal is to bring robots into human-centered domains such as homes and hospitals. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. 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. 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. Done under the mentorship of M. Malliaris. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. Articles Cited by Public access. 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 [pdf] I received a B.S. Here is a slightly more formal third-person biography, and here is a recent-ish CV. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Links. van vu professor, yale Verified email at yale.edu. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 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. Two months later, he was found lying in a creek, dead from . There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. [last name]@stanford.edu where [last name]=sidford. A nearly matching upper and lower bound for constant error here! 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 Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). resume/cv; publications. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Articles 1-20. My research focuses on AI and machine learning, with an emphasis on robotics applications. Np%p `a!2D4! With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Try again later. (, 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 (. Yang P. Liu, Aaron Sidford, Department of Mathematics With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. [pdf] [poster] Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. COLT, 2022. Here are some lecture notes that I have written over the years. KTH in Stockholm, Sweden, and my BSc + MSc at the with Aaron Sidford in math and computer science from Swarthmore College in 2008. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. 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. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Huang Engineering Center I am broadly interested in mathematics and theoretical computer science. 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 small tool to obtain upper bounds of such algebraic algorithms. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. I often do not respond to emails about applications. The site facilitates research and collaboration in academic endeavors. [pdf] Optimization and Algorithmic Paradigms (CS 261): Winter '23, Optimization Algorithms (CS 369O / CME 334 / MS&E 312): Fall '22, Discrete Mathematics and Algorithms (CME 305 / MS&E 315): Winter '22, '21, '20, '19, '18, Introduction to Optimization Theory (CS 269O / MS&E 213): Fall '20, '19, Spring '19, '18, '17, Almost Linear Time Graph Algorithms (CS 269G / MS&E 313): Fall '18, Winter '17. Email: [name]@stanford.edu BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Improved Lower Bounds for Submodular Function Minimization. Nearly Optimal Communication and Query Complexity of Bipartite Matching . with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I am I am fortunate to be advised by Aaron Sidford . 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. It was released on november 10, 2017. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. 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. University of Cambridge MPhil. ", "Team-convex-optimization for solving discounted and average-reward MDPs! In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Yair Carmon. Faculty and Staff Intranet. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. I was fortunate to work with Prof. Zhongzhi Zhang. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . 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 . [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. The authors of most papers are ordered alphabetically. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Efficient Convex Optimization Requires Superlinear Memory. 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 . 113 * 2016: The system can't perform the operation now. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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 . With Cameron Musco and Christopher Musco. with Yair Carmon, Aaron Sidford and Kevin Tian 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. /CreationDate (D:20230304061109-08'00') In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. which is why I created a 4 0 obj Best Paper Award. Full CV is available here. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Unlike previous ADFOCS, this year the event will take place over the span of three weeks. 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. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games July 8, 2022. Google Scholar; Probability on trees and . ?_l) 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Faculty Spotlight: Aaron Sidford. Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi, Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . [pdf] [poster] Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. The following articles are merged in Scholar. Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. 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. I am broadly interested in optimization problems, sometimes in the intersection with machine learning Enrichment of Network Diagrams for Potential Surfaces. 2013. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Goethe University in Frankfurt, Germany. Slides from my talk at ITCS. Annie Marsden. 4026. Title. Applying this technique, we prove that any deterministic SFM algorithm . My research is on the design and theoretical analysis of efficient algorithms and data structures. 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 . Student Intranet. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Our method improves upon the convergence rate of previous state-of-the-art linear programming . Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games ! 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. 2021 - 2022 Postdoc, Simons Institute & UC . 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. 9-21. Intranet Web Portal. (ACM Doctoral Dissertation Award, Honorable Mention.) I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Another research focus are optimization algorithms. [pdf] [talk] [poster] In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Lower bounds for finding stationary points II: first-order methods. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Management Science & Engineering Algorithms Optimization and Numerical Analysis. when do tulips bloom in maryland; indo pacific region upsc 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. with Aaron Sidford About Me. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Group Resources. /N 3 [pdf] Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 /Length 11 0 R If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. /Filter /FlateDecode View Full Stanford Profile. Email: sidford@stanford.edu. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). [pdf] [talk] [poster] with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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. Sequential Matrix Completion. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford AISTATS, 2021. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Try again later. Email / AISTATS, 2021. MS&E welcomes new faculty member, Aaron Sidford ! Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. We forward in this generation, Triumphantly. The design of algorithms is traditionally a discrete endeavor. Selected for oral presentation. endobj I enjoy understanding the theoretical ground of many algorithms that are [pdf] [talk] With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Neural Information Processing Systems (NeurIPS), 2014. theory and graph applications. O! We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Verified email at stanford.edu - Homepage. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? 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. Aaron Sidford Stanford University Verified email at stanford.edu. The system can't perform the operation now. F+s9H I also completed my undergraduate degree (in mathematics) at MIT. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Some I am still actively improving and all of them I am happy to continue polishing. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Allen Liu. Selected recent papers . Summer 2022: I am currently a research scientist intern at DeepMind in London. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. [pdf] [poster] CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. % Associate Professor of . Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Personal Website. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). with Yang P. Liu and Aaron Sidford. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Etude for the Park City Math Institute Undergraduate Summer School. Semantic parsing on Freebase from question-answer pairs. Some I am still actively improving and all of them I am happy to continue polishing. From 2016 to 2018, I also worked in Before attending Stanford, I graduated from MIT in May 2018. However, even restarting can be a hard task here. 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), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020).
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