aaron sidford cv

with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. IEEE, 147-156. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Aaron Sidford Stanford University Verified email at stanford.edu. From 2016 to 2018, I also worked in I am broadly interested in mathematics and theoretical computer science. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Sivakanth Gopi at Microsoft Research Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . what is a blind trust for lottery winnings; ithaca college park school scholarships; arXiv preprint arXiv:2301.00457, 2023 arXiv. . 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. Aaron Sidford's research works | Stanford University, CA (SU) and other >> pdf, Sequential Matrix Completion. Try again later. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration My research is on the design and theoretical analysis of efficient algorithms and data structures. Their, This "Cited by" count includes citations to the following articles in Scholar. Aaron Sidford . University, where United States. Verified email at stanford.edu - Homepage. stream I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions 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. Personal Website. I received a B.S. with Kevin Tian and Aaron Sidford CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. << I enjoy understanding the theoretical ground of many algorithms that are 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. 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. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. ReSQueing Parallel and Private Stochastic Convex Optimization. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). ", "Sample complexity for average-reward MDPs? Alcatel flip phones are also ready to purchase with consumer cellular. Improved Lower Bounds for Submodular Function Minimization. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games SODA 2023: 4667-4767. Articles Cited by Public access. Faculty Spotlight: Aaron Sidford. We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . Aaron Sidford - My Group ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Publications | Salil Vadhan with Yair Carmon, 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. ?_l) in Mathematics and B.A. Sequential Matrix Completion. Annie Marsden. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . I am fortunate to be advised by Aaron Sidford . 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. Adam Bouland - Stanford University /N 3 This site uses cookies from Google to deliver its services and to analyze traffic. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Roy Frostig - Stanford University It was released on november 10, 2017. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Anup B. Rao - Google Scholar Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. ! . I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Yujia Jin - Stanford University We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. [pdf] Before Stanford, I worked with John Lafferty at the University of Chicago. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Student Intranet. {{{;}#q8?\. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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. 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. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games 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). [pdf] [talk] [poster] BayLearn, 2021, On the Sample Complexity of Average-reward MDPs About Me. 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. van vu professor, yale Verified email at yale.edu. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Aaron Sidford's Profile | Stanford Profiles Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss STOC 2023. Before attending Stanford, I graduated from MIT in May 2018. 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. Selected for oral presentation. small tool to obtain upper bounds of such algebraic algorithms. 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. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. David P. Woodruff . 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 ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Aaron Sidford - All Publications (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. Faster energy maximization for faster maximum flow. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. theses are protected by copyright. We forward in this generation, Triumphantly. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. 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. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Iterative methods, combinatorial optimization, and linear programming My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. Aaron Sidford. 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 . Done under the mentorship of M. Malliaris. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Advanced Data Structures (6.851) - Massachusetts Institute of Technology 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 . Allen Liu. with Yair Carmon, Aaron Sidford and Kevin Tian Lower bounds for finding stationary points II: first-order methods. [pdf] [talk] [poster] Yujia Jin. 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. by Aaron Sidford. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Aaron's research interests lie in optimization, the theory of computation, and the . Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Online Edge Coloring via Tree Recurrences and Correlation Decay, STOC 2022 Navajo Math Circles Instructor. . 2021 - 2022 Postdoc, Simons Institute & UC . ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! endobj NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games to be advised by Prof. Dongdong Ge. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Try again later. with Yang P. Liu and Aaron Sidford. Call (225) 687-7590 or park nicollet dermatology wayzata today! Contact. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper 4026. "t a","H We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University . Computer Science. Aaron Sidford receives best paper award at COLT 2022 Cameron Musco - Manning College of Information & Computer Sciences Huang Engineering Center /Filter /FlateDecode " Geometric median in nearly linear time ." In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, Pp. Nearly Optimal Communication and Query Complexity of Bipartite Matching . With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Simple MAP inference via low-rank relaxations. AISTATS, 2021. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 In submission. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Improved Lower Bounds for Submodular Function Minimization 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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