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University, Research Institute for Interdisciplinary Sciences (RIIS) at IEEE, 147-156. 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 . 9-21. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. with Yair Carmon, Kevin Tian and Aaron Sidford with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Huang Engineering Center Before attending Stanford, I graduated from MIT in May 2018. Another research focus are optimization algorithms. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) STOC 2023. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Aaron's research interests lie in optimization, the theory of computation, and the . with Yair Carmon, Aaron Sidford and Kevin Tian with Aaron Sidford Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! My CV. ", "Team-convex-optimization for solving discounted and average-reward MDPs! To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. 4 0 obj F+s9H 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. Journal of Machine Learning Research, 2017 (arXiv). Management Science & Engineering In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 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). 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. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Google Scholar Digital Library; Russell Lyons and Yuval Peres. 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 . /Length 11 0 R Associate Professor of . 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. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Enrichment of Network Diagrams for Potential Surfaces. 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). to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching with Yair Carmon, Aaron Sidford and Kevin Tian how . Email: [name]@stanford.edu The site facilitates research and collaboration in academic endeavors. ", "A short version of the conference publication under the same title. 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. AISTATS, 2021. Links. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Try again later. Etude for the Park City Math Institute Undergraduate Summer School. with Yair Carmon, Aaron Sidford and Kevin Tian 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 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. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Stanford, CA 94305 missouri noodling association president cnn. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. ! Navajo Math Circles Instructor. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. to be advised by Prof. Dongdong Ge. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA stream theses are protected by copyright. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 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. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. We also provide two . >> Aleksander Mdry; Generalized preconditioning and network flow problems small tool to obtain upper bounds of such algebraic algorithms. Student Intranet. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Before attending Stanford, I graduated from MIT in May 2018. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." "t a","H Unlike previous ADFOCS, this year the event will take place over the span of three weeks. pdf, Sequential Matrix Completion. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization [pdf] [poster] Yang P. Liu, Aaron Sidford, Department of Mathematics Thesis, 2016. pdf. [pdf] Anup B. Rao. Office: 380-T 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. The following articles are merged in Scholar. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Contact. << COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. when do tulips bloom in maryland; indo pacific region upsc 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 . ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. From 2016 to 2018, I also worked in Personal Website. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. ?_l) with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford [pdf] Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Summer 2022: I am currently a research scientist intern at DeepMind in London. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. ICML, 2016. Semantic parsing on Freebase from question-answer pairs. About Me. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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 . Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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 ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. This improves upon previous best known running times of O (nr1.5T-ind) due to Cunningham in 1986 and (n2T-ind+n3) due to Lee, Sidford, and Wong in 2015. [pdf] [slides] I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. by Aaron Sidford. David P. Woodruff . Articles 1-20. Np%p `a!2D4! Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Stanford University. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games << ", Applied Math at Fudan Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . My long term goal is to bring robots into human-centered domains such as homes and hospitals. 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. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 2016. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. arXiv preprint arXiv:2301.00457, 2023 arXiv. If you see any typos or issues, feel free to email me. /N 3 I was fortunate to work with Prof. Zhongzhi Zhang. A nearly matching upper and lower bound for constant error here! sidford@stanford.edu. . Try again later. With Cameron Musco and Christopher Musco. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University with Aaron Sidford I regularly advise Stanford students from a variety of departments. in math and computer science from Swarthmore College in 2008. 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. 2023. . with Vidya Muthukumar and Aaron Sidford In submission. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Slides from my talk at ITCS. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Publications and Preprints. ", "Sample complexity for average-reward MDPs? With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Algorithms Optimization and Numerical Analysis. United States. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. COLT, 2022. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Faster energy maximization for faster maximum flow. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Secured intranet portal for faculty, staff and students. [pdf] [talk] [poster] Email: sidford@stanford.edu. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss With Yair Carmon, John C. Duchi, and Oliver Hinder. 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 . ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. " 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. Selected recent papers . ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. View Full Stanford Profile. Aaron Sidford. (, 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 (. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. 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. Some I am still actively improving and all of them I am happy to continue polishing. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& [pdf] The authors of most papers are ordered alphabetically. [pdf] Call (225) 687-7590 or park nicollet dermatology wayzata today! (ACM Doctoral Dissertation Award, Honorable Mention.) ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. 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
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