To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Follow. with Yair Carmon, Kevin Tian and Aaron Sidford ICML, 2016. >> Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. stream Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 [pdf] [PDF] Faster Algorithms for Computing the Stationary Distribution 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 Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 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 . Done under the mentorship of M. Malliaris. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Aaron Sidford's Homepage - Stanford University Improved Lower Bounds for Submodular Function Minimization AISTATS, 2021. Kirankumar Shiragur | Data Science Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Here are some lecture notes that I have written over the years. Simple MAP inference via low-rank relaxations. 2023. . missouri noodling association president cnn. >> Google Scholar; Probability on trees and . how . Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Faculty Spotlight: Aaron Sidford - Management Science and Engineering Aaron Sidford Stanford University Verified email at stanford.edu. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods 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. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. A nearly matching upper and lower bound for constant error here! of practical importance. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. A Faster Algorithm for Linear Programming and the Maximum Flow Problem II We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . with Kevin Tian and Aaron Sidford [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 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. CME 305/MS&E 316: Discrete Mathematics and Algorithms 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. 2021 - 2022 Postdoc, Simons Institute & UC . Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. [pdf] [talk] [poster] 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. Stanford University Stanford, CA 94305 Yang P. Liu, Aaron Sidford, Department of Mathematics Lower Bounds for Finding Stationary Points II: First-Order Methods Try again later. [pdf] Selected recent papers . Best Paper Award. David P. Woodruff . with Yair Carmon, Aaron Sidford and Kevin Tian I regularly advise Stanford students from a variety of departments. /Producer (Apache FOP Version 1.0) Neural Information Processing Systems (NeurIPS), 2014. 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. In this talk, I will present a new algorithm for solving linear programs. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. the Operations Research group. Contact. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Yujia Jin. with Vidya Muthukumar and Aaron Sidford 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. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. 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 . Jan van den Brand Email: [name]@stanford.edu ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Advanced Data Structures (6.851) - Massachusetts Institute of Technology ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Aleksander Mdry; Generalized preconditioning and network flow problems David P. Woodruff - Carnegie Mellon University Some I am still actively improving and all of them I am happy to continue polishing. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. aaron sidford cvis sea bass a bony fish to eat. PDF Daogao Liu Lower bounds for finding stationary points I, Accelerated Methods for NonConvex Optimization, SIAM Journal on Optimization, 2018 (arXiv), Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Information about your use of this site is shared with Google. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries 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). This site uses cookies from Google to deliver its services and to analyze traffic. Anup B. Rao - Google Scholar 113 * 2016: The system can't perform the operation now. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). with Yang P. Liu and Aaron Sidford. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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 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. Aaron Sidford's Profile | Stanford Profiles 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. About - Annie Marsden I am a senior researcher in the Algorithms group at Microsoft Research Redmond. She was 19 years old and looking - freewareppc.com International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Accelerated Methods for NonConvex Optimization | Semantic Scholar Some I am still actively improving and all of them I am happy to continue polishing. Full CV is available here. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 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. Call (225) 687-7590 or park nicollet dermatology wayzata today! 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching 9-21. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Aaron Sidford - My Group Mail Code. Allen Liu - GitHub Pages data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Aaron Sidford Goethe University in Frankfurt, Germany. The following articles are merged in Scholar. Articles 1-20. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time UGTCS ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. F+s9H 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. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Faster energy maximization for faster maximum flow. Many of my results use fast matrix multiplication Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Sivakanth Gopi at Microsoft Research in math and computer science from Swarthmore College in 2008. I was fortunate to work with Prof. Zhongzhi Zhang. Abstract. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss /Creator (Apache FOP Version 1.0) International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG In Sidford's dissertation, Iterative Methods, Combinatorial . Aaron's research interests lie in optimization, the theory of computation, and the . I also completed my undergraduate degree (in mathematics) at MIT. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). I received a B.S. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Improved Lower Bounds for Submodular Function Minimization. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Enrichment of Network Diagrams for Potential Surfaces. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Yujia Jin. by Aaron Sidford. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. My research focuses on AI and machine learning, with an emphasis on robotics applications. Yang P. Liu - GitHub Pages Journal of Machine Learning Research, 2017 (arXiv). Aaron Sidford. Aaron Sidford - Stanford University 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. 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 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. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. aaron sidford cv Before attending Stanford, I graduated from MIT in May 2018. In each setting we provide faster exact and approximate algorithms. I am broadly interested in optimization problems, sometimes in the intersection with machine learning 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Intranet Web Portal. % Publications and Preprints. University of Cambridge MPhil. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. {{{;}#q8?\. The design of algorithms is traditionally a discrete endeavor. /Filter /FlateDecode Office: 380-T Research Institute for Interdisciplinary Sciences (RIIS) at Cameron Musco - Manning College of Information & Computer Sciences "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. CoRR abs/2101.05719 ( 2021 ) 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.