The site facilitates research and collaboration in academic endeavors. Try again later. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. with Yair Carmon, Aaron Sidford and Kevin Tian We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). sidford@stanford.edu. Yair Carmon. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Source: appliancesonline.com.au. The authors of most papers are ordered alphabetically. 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. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Lower bounds for finding stationary points II: first-order methods. with Aaron Sidford I am [pdf] [slides] /N 3 I completed my PhD at with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. (, 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 (. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Yang P. Liu, Aaron Sidford, Department of Mathematics 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. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Some I am still actively improving and all of them I am happy to continue polishing. I also completed my undergraduate degree (in mathematics) at MIT. /CreationDate (D:20230304061109-08'00') [pdf] [talk] [poster] aaron sidford cvis sea bass a bony fish to eat. << However, even restarting can be a hard task here. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Information about your use of this site is shared with Google. COLT, 2022. Computer Science. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! [pdf] [talk] Aleksander Mdry; Generalized preconditioning and network flow problems Selected for oral presentation. Group Resources. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Verified email at stanford.edu - Homepage. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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). They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Selected recent papers . Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent ", "A short version of the conference publication under the same title. Many of my results use fast matrix multiplication 2021 - 2022 Postdoc, Simons Institute & UC . I am an Assistant Professor in the School of Computer Science at Georgia Tech. [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. 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. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. In Symposium on Theory of Computing (STOC 2020) (arXiv), Constant Girth Approximation for Directed Graphs in Subquadratic Time, With Shiri Chechik, Yang P. Liu, and Omer Rotem, Leverage Score Sampling for Faster Accelerated Regression and ERM, With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli, In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv), Near-optimal Approximate Discrete and Continuous Submodular Function Minimization, In Symposium on Discrete Algorithms (SODA 2020) (arXiv), Fast and Space Efficient Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos, In Conference on Neural Information Processing Systems (NeurIPS 2019), Complexity of Highly Parallel Non-Smooth Convex Optimization, With Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, A Direct (1/) Iteration Parallel Algorithm for Optimal Transport, In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv), A General Framework for Efficient Symmetric Property Estimation, With Moses Charikar and Kirankumar Shiragur, Parallel Reachability in Almost Linear Work and Square Root Depth, In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv), With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong, Deterministic Approximation of Random Walks in Small Space, With Jack Murtagh, Omer Reingold, and Salil P. Vadhan, In International Workshop on Randomization and Computation (RANDOM 2019), A Rank-1 Sketch for Matrix Multiplicative Weights, With Yair Carmon, John C. Duchi, and Kevin Tian, In Conference on Learning Theory (COLT 2019) (arXiv), Near-optimal method for highly smooth convex optimization, Efficient profile maximum likelihood for universal symmetric property estimation, In Symposium on Theory of Computing (STOC 2019) (arXiv), Memory-sample tradeoffs for linear regression with small error, Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications, With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi, In Symposium on Discrete Algorithms (SODA 2019) (arXiv), Exploiting Numerical Sparsity for Efficient Learning: Faster Eigenvector Computation and Regression, In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv), Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model, With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye, Coordinate Methods for Accelerating Regression and Faster Approximate Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2018), Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations, With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao, In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv), Efficient Convex Optimization with Membership Oracles, In Conference on Learning Theory (COLT 2018) (arXiv), Accelerating Stochastic Gradient Descent for Least Squares Regression, With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli, Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. Aaron Sidford Stanford University Verified email at stanford.edu. Yujia Jin. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. rl1 ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Personal Website. [pdf] IEEE, 147-156. . Student Intranet. of practical importance. 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. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. 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. with Arun Jambulapati, Aaron Sidford and Kevin Tian I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Stanford University International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Roy Frostig, Sida Wang, Percy Liang, Chris Manning. 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. Yujia Jin. SHUFE, where I was fortunate Title. Stanford, CA 94305 We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. From 2016 to 2018, I also worked in publications by categories in reversed chronological order. [pdf] Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 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 My interests are in the intersection of algorithms, statistics, optimization, and machine learning. University of Cambridge MPhil. Yin Tat Lee and Aaron Sidford. Done under the mentorship of M. Malliaris. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. with Aaron Sidford 2023. . I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. /Producer (Apache FOP Version 1.0) 4 0 obj when do tulips bloom in maryland; indo pacific region upsc With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ReSQueing Parallel and Private Stochastic Convex Optimization. F+s9H Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Before Stanford, I worked with John Lafferty at the University of Chicago. what is a blind trust for lottery winnings; ithaca college park school scholarships; Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Another research focus are optimization algorithms. 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. (ACM Doctoral Dissertation Award, Honorable Mention.) With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am broadly interested in mathematics and theoretical computer science. Np%p `a!2D4! 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 %PDF-1.4 KTH in Stockholm, Sweden, and my BSc + MSc at the Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. . I graduated with a PhD from Princeton University in 2018. 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. the Operations Research group. I am broadly interested in mathematics and theoretical computer science. With Cameron Musco and Christopher Musco. The system can't perform the operation now. [pdf] [talk] They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . with Vidya Muthukumar and Aaron Sidford Anup B. Rao. In International Conference on Machine Learning (ICML 2016). to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching My research focuses on AI and machine learning, with an emphasis on robotics applications. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. 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, riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries [pdf] Here is a slightly more formal third-person biography, and here is a recent-ish CV. Before attending Stanford, I graduated from MIT in May 2018. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . With Yair Carmon, John C. Duchi, and Oliver Hinder. July 8, 2022. Assistant Professor of Management Science and Engineering and of Computer Science. Google Scholar Digital Library; Russell Lyons and Yuval Peres. 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. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Algorithms Optimization and Numerical Analysis. 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. The design of algorithms is traditionally a discrete endeavor. 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. University, where SODA 2023: 4667-4767. He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. University, Research Institute for Interdisciplinary Sciences (RIIS) at International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG resume/cv; publications. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. 5 0 obj Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. I was fortunate to work with Prof. Zhongzhi Zhang. with Yair Carmon, Aaron Sidford and Kevin Tian van vu professor, yale Verified email at yale.edu.
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