aaron sidford cv
Sivakanth Gopi at Microsoft Research "t a","H 2016. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). Thesis, 2016. pdf. United States. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. It was released on november 10, 2017. Computer Science. with Yair Carmon, Arun Jambulapati and Aaron Sidford I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Secured intranet portal for faculty, staff and students. [pdf] [talk] [poster] ICML, 2016. We forward in this generation, Triumphantly. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. " 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. Sequential Matrix Completion. Adam Bouland - Stanford University Gregory Valiant Homepage - Stanford University Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Goethe University in Frankfurt, Germany. I graduated with a PhD from Princeton University in 2018. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). ReSQueing Parallel and Private Stochastic Convex Optimization. Here is a slightly more formal third-person biography, and here is a recent-ish CV. Aaron Sidford's Homepage - Stanford University 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. with Arun Jambulapati, Aaron Sidford and Kevin Tian I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. in Mathematics and B.A. SHUFE, where I was fortunate Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. I also completed my undergraduate degree (in mathematics) at MIT. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 AISTATS, 2021. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). In each setting we provide faster exact and approximate algorithms. with Aaron Sidford in Chemistry at the University of Chicago. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Selected recent papers . to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. 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. with Yair Carmon, Aaron Sidford and Kevin Tian Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. with Yair Carmon, Aaron Sidford and Kevin Tian [pdf] [talk] IEEE, 147-156. Title. % Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Office: 380-T Yujia Jin. Before attending Stanford, I graduated from MIT in May 2018. arXiv preprint arXiv:2301.00457, 2023 arXiv. View Full Stanford Profile. Personal Website. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Google Scholar; Probability on trees and . 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Aaron Sidford - All Publications 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. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. 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. Anup B. Rao. Allen Liu - GitHub Pages SODA 2023: 5068-5089. 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. 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 is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. The system can't perform the operation now. Simple MAP inference via low-rank relaxations. In submission. UGTCS Student Intranet. with Aaron Sidford Before attending Stanford, I graduated from MIT in May 2018. Source: appliancesonline.com.au. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Another research focus are optimization algorithms. CV (last updated 01-2022): PDF Contact. Aaron Sidford's Profile | Stanford Profiles 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. Improved Lower Bounds for Submodular Function Minimization with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian with Aaron Sidford 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. ", Applied Math at Fudan Improves the stochas-tic convex optimization problem in parallel and DP setting. [pdf] publications | Daogao Liu Source: www.ebay.ie 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. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Assistant Professor of Management Science and Engineering and of Computer Science. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Articles Cited by Public access. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. 4026. 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. About - Annie Marsden Our method improves upon the convergence rate of previous state-of-the-art linear programming . Publications | Salil Vadhan ", "Sample complexity for average-reward MDPs? dblp: Yin Tat Lee Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Aaron Sidford. 22nd Max Planck Advanced Course on the Foundations of Computer Science I regularly advise Stanford students from a variety of departments. Enrichment of Network Diagrams for Potential Surfaces. 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. Call (225) 687-7590 or park nicollet dermatology wayzata today! Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Full CV is available here. . In International Conference on Machine Learning (ICML 2016). stream Mary Wootters - Google 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. We also provide two . We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Faster Matroid Intersection Princeton University Yang P. Liu - GitHub Pages ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods /N 3 I am broadly interested in mathematics and theoretical computer science. . aaron sidford cv with Yair Carmon, Aaron Sidford and Kevin Tian >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. aaron sidford cv natural fibrin removal - libiot.kku.ac.th To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. theses are protected by copyright. Links. 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. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Faculty Spotlight: Aaron Sidford. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions With Cameron Musco and Christopher Musco. ", "Team-convex-optimization for solving discounted and average-reward MDPs! My long term goal is to bring robots into human-centered domains such as homes and hospitals. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I am broadly interested in mathematics and theoretical computer science. PDF Daogao Liu Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . I am an Assistant Professor in the School of Computer Science at Georgia Tech. 2016. small tool to obtain upper bounds of such algebraic algorithms. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! (, 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. From 2016 to 2018, I also worked in Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Neural Information Processing Systems (NeurIPS), 2014. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. [pdf] [poster] ", "A short version of the conference publication under the same title. If you see any typos or issues, feel free to email me. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! [PDF] Faster Algorithms for Computing the Stationary Distribution NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games I am broadly interested in optimization problems, sometimes in the intersection with machine learning 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. I am fortunate to be advised by Aaron Sidford . I enjoy understanding the theoretical ground of many algorithms that are [pdf] [talk] [poster] 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. Yin Tat Lee and Aaron Sidford. Aaron Sidford University, Research Institute for Interdisciplinary Sciences (RIIS) at . when do tulips bloom in maryland; indo pacific region upsc Semantic parsing on Freebase from question-answer pairs. COLT, 2022. Iterative methods, combinatorial optimization, and linear programming 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. sidford@stanford.edu. with Vidya Muthukumar and Aaron Sidford In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) aaron sidford cvis sea bass a bony fish to eat. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Group Resources. Email: sidford@stanford.edu. 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. Aaron Sidford - Selected Publications Aaron's research interests lie in optimization, the theory of computation, and the . Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time /Creator (Apache FOP Version 1.0) with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Follow. 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. Two months later, he was found lying in a creek, dead from . Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Overview This class will introduce the theoretical foundations of discrete mathematics and 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. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Abstract. 4 0 obj in math and computer science from Swarthmore College in 2008. missouri noodling association president cnn. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 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). Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Improved Lower Bounds for Submodular Function Minimization. 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 by 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.
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