Improves the stochas-tic convex optimization problem in parallel and DP setting. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. " 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. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. 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 . I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. Before Stanford, I worked with John Lafferty at the University of Chicago. Goethe University in Frankfurt, Germany. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). endobj [pdf] Full CV is available here. 2023. . "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. University of Cambridge MPhil. CV (last updated 01-2022): PDF Contact. 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. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. (, 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 (. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Applying this technique, we prove that any deterministic SFM algorithm . << My long term goal is to bring robots into human-centered domains such as homes and hospitals. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Aaron Sidford. 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 Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! 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 . Faster energy maximization for faster maximum flow. [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. My CV. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. 475 Via Ortega 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. 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. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). missouri noodling association president cnn. With Yair Carmon, John C. Duchi, and Oliver Hinder. Before attending Stanford, I graduated from MIT in May 2018. [pdf] [talk] [poster] Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. >> Here is a slightly more formal third-person biography, and here is a recent-ish CV. Follow. Before attending Stanford, I graduated from MIT in May 2018. 5 0 obj ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. [pdf] SODA 2023: 5068-5089. Aleksander Mdry; Generalized preconditioning and network flow problems Simple MAP inference via low-rank relaxations. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Conference on Learning Theory (COLT), 2015. Here are some lecture notes that I have written over the years. AISTATS, 2021. with Yang P. Liu and Aaron Sidford. If you see any typos or issues, feel free to email me. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I am broadly interested in mathematics and theoretical computer science. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). [pdf] In International Conference on Machine Learning (ICML 2016). 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. with Aaron Sidford Abstract. One research focus are dynamic algorithms (i.e. /Creator (Apache FOP Version 1.0) . sidford@stanford.edu. 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. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian 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. The following articles are merged in Scholar. Links. Associate Professor of . with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Contact. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford with Yair Carmon, Arun Jambulapati and Aaron Sidford [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Office: 380-T Selected for oral presentation. The design of algorithms is traditionally a discrete endeavor. [pdf] [talk] [poster] with Aaron Sidford Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 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. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. ", "A short version of the conference publication under the same title. Mail Code. of practical importance. COLT, 2022. I received a B.S. I am an Assistant Professor in the School of Computer Science at Georgia Tech. >> Enrichment of Network Diagrams for Potential Surfaces. 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 . with Aaron Sidford 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. Yin Tat Lee and Aaron Sidford. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. I regularly advise Stanford students from a variety of departments. 2021. However, many advances have come from a continuous viewpoint. in Mathematics and B.A. However, even restarting can be a hard task here. stream IEEE, 147-156. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Main Menu. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). KTH in Stockholm, Sweden, and my BSc + MSc at the In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. [pdf] University, where Yair Carmon. [pdf] [poster] Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Assistant Professor of Management Science and Engineering and of Computer Science. ?_l) The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Faculty Spotlight: Aaron Sidford. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ ", "Team-convex-optimization for solving discounted and average-reward MDPs! Yang P. Liu, Aaron Sidford, Department of Mathematics 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. MS&E welcomes new faculty member, Aaron Sidford ! van vu professor, yale Verified email at yale.edu. The authors of most papers are ordered alphabetically. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Sequential Matrix Completion. by Aaron Sidford. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). to be advised by Prof. Dongdong Ge. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Stanford University. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. 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's 143 research works with 2,861 citations and 1,915 reads, including: Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification 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. 2013. 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. 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. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Some I am still actively improving and all of them I am happy to continue polishing. 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. ", "Sample complexity for average-reward MDPs? ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Aaron's research interests lie in optimization, the theory of computation, and the . O! STOC 2023. I enjoy understanding the theoretical ground of many algorithms that are resume/cv; publications. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Aaron Sidford Stanford University Verified email at stanford.edu. Some I am still actively improving and all of them I am happy to continue polishing. with Yair Carmon, Aaron Sidford and Kevin Tian Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. 2017. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. aaron sidford cvis sea bass a bony fish to eat. ! I also completed my undergraduate degree (in mathematics) at MIT. In submission. what is a blind trust for lottery winnings; ithaca college park school scholarships; I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. The site facilitates research and collaboration in academic endeavors. Secured intranet portal for faculty, staff and students. Information about your use of this site is shared with Google. Computer Science. Try again later. 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. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. In each setting we provide faster exact and approximate algorithms. AISTATS, 2021. University, Research Institute for Interdisciplinary Sciences (RIIS) at when do tulips bloom in maryland; indo pacific region upsc United States. . Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . with Kevin Tian and Aaron Sidford SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). %PDF-1.4 Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Unlike previous ADFOCS, this year the event will take place over the span of three weeks. with Arun Jambulapati, Aaron Sidford and Kevin Tian Verified email at stanford.edu - Homepage. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. {{{;}#q8?\. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). About Me. We forward in this generation, Triumphantly. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. with Yair Carmon, Kevin Tian and Aaron Sidford 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. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. /Filter /FlateDecode Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. pdf, Sequential Matrix Completion. Best Paper Award. Efficient Convex Optimization Requires Superlinear Memory. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. A nearly matching upper and lower bound for constant error here! Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Etude for the Park City Math Institute Undergraduate Summer School. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Improved Lower Bounds for Submodular Function Minimization. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . [pdf] [poster] My research focuses on AI and machine learning, with an emphasis on robotics applications. 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 My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV).

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