! Title. I received a B.S. Mary Wootters - Google ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. We forward in this generation, Triumphantly. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. publications by categories in reversed chronological order. [pdf] Aaron Sidford Stanford University Verified email at stanford.edu. 2017. [pdf] I enjoy understanding the theoretical ground of many algorithms that are Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova . theses are protected by copyright. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. with Yair Carmon, Aaron Sidford and Kevin Tian If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration CoRR abs/2101.05719 ( 2021 ) My interests are in the intersection of algorithms, statistics, optimization, and machine learning. . Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Applying this technique, we prove that any deterministic SFM algorithm . 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 CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. [pdf] [poster] With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). If you see any typos or issues, feel free to email me. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Follow. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford My long term goal is to bring robots into human-centered domains such as homes and hospitals. 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. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space "t a","H ", "Team-convex-optimization for solving discounted and average-reward MDPs! Secured intranet portal for faculty, staff and students. Sequential Matrix Completion. 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. Jan van den Brand To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. I was fortunate to work with Prof. Zhongzhi Zhang. Publications and Preprints. Slides from my talk at ITCS. Try again later. About Me. van vu professor, yale Verified email at yale.edu. 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 "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. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Faculty Spotlight: Aaron Sidford. United States. Cameron Musco - Manning College of Information & Computer Sciences Assistant Professor of Management Science and Engineering and of Computer Science. " 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. 2013. Management Science & Engineering /Filter /FlateDecode Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . ?_l) We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Articles 1-20. Some I am still actively improving and all of them I am happy to continue polishing. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. If you see any typos or issues, feel free to email me. The design of algorithms is traditionally a discrete endeavor. Conference on Learning Theory (COLT), 2015. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Full CV is available here. 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. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 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] aaron sidford cv natural fibrin removal - libiot.kku.ac.th in math and computer science from Swarthmore College in 2008. Huang Engineering Center Yujia Jin. [last name]@stanford.edu where [last name]=sidford. Yin Tat Lee and Aaron Sidford. 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 Yair Carmon, Kevin Tian and Aaron Sidford This is the academic homepage of Yang Liu (I publish under Yang P. Liu). ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). . The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Aaron Sidford | Stanford Online the Operations Research group. endobj Selected for oral presentation. Yang P. Liu, Aaron Sidford, Department of Mathematics [pdf] Allen Liu. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries With Yair Carmon, John C. Duchi, and Oliver Hinder. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! One research focus are dynamic algorithms (i.e. Aaron Sidford - My Group ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). 2023. . I also completed my undergraduate degree (in mathematics) at MIT. Aaron Sidford's Homepage - Stanford University Some I am still actively improving and all of them I am happy to continue polishing. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Before attending Stanford, I graduated from MIT in May 2018. Aaron Sidford | Management Science and Engineering University of Cambridge MPhil. 113 * 2016: The system can't perform the operation now. /N 3 [pdf] pdf, Sequential Matrix Completion. 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. Annie Marsden. Goethe University in Frankfurt, Germany. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Email / 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. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Iterative methods, combinatorial optimization, and linear programming Stanford University. Lower bounds for finding stationary points II: first-order methods. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). >> Group Resources. Etude for the Park City Math Institute Undergraduate Summer School. Improved Lower Bounds for Submodular Function Minimization. missouri noodling association president cnn. aaron sidford cv Computer Science. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. My CV. . rl1 Faculty and Staff Intranet. (, 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 (. The system can't perform the operation now. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Anup B. Rao. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. I am fortunate to be advised by Aaron Sidford. Links. A nearly matching upper and lower bound for constant error here! Improved Lower Bounds for Submodular Function Minimization With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am broadly interested in mathematics and theoretical computer science. stream ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Efficient Convex Optimization Requires Superlinear Memory. The following articles are merged in Scholar. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. how . Articles Cited by Public access. The site facilitates research and collaboration in academic endeavors. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Improves the stochas-tic convex optimization problem in parallel and DP setting. Yujia Jin - Stanford University 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. Aaron Sidford - Selected Publications View Full Stanford Profile. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Aaron Sidford receives best paper award at COLT 2022 MS&E213 / CS 269O - Introduction to Optimization Theory PDF Daogao Liu ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Nearly Optimal Communication and Query Complexity of Bipartite Matching . 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. KTH in Stockholm, Sweden, and my BSc + MSc at the I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. This site uses cookies from Google to deliver its services and to analyze traffic. sidford@stanford.edu. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Another research focus are optimization algorithms. Advanced Data Structures (6.851) - Massachusetts Institute of Technology Verified email at stanford.edu - Homepage. Np%p `a!2D4! ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? 2021 - 2022 Postdoc, Simons Institute & UC . ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Aaron Sidford. 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. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games to be advised by Prof. Dongdong Ge. 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. 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. 5 0 obj Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Try again later. July 8, 2022. /Producer (Apache FOP Version 1.0) Faster energy maximization for faster maximum flow. CME 305/MS&E 316: Discrete Mathematics and Algorithms Student Intranet. 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 In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Google Scholar; Probability on trees and . Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. In submission. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Gregory Valiant Homepage - Stanford University Contact. (ACM Doctoral Dissertation Award, Honorable Mention.) I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. My research focuses on AI and machine learning, with an emphasis on robotics applications. Before Stanford, I worked with John Lafferty at the University of Chicago. 4 0 obj ", "Sample complexity for average-reward MDPs? 2021. 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. 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 mathematics and theoretical computer science. 4026. /Length 11 0 R 2016. STOC 2023. 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. Neural Information Processing Systems (NeurIPS), 2014. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. By using this site, you agree to its use of cookies. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. [pdf] [talk] [poster] Google Scholar Digital Library; Russell Lyons and Yuval Peres. with Vidya Muthukumar and Aaron Sidford With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. AISTATS, 2021. She was 19 years old and looking - freewareppc.com 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 Yair Carmon, Aaron Sidford and Kevin Tian % Microsoft Research Faculty Fellowship 2020: Researchers in academia at 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). Aaron Sidford - Stanford University I am SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Aaron Sidford - Home - Author DO Series Research Institute for Interdisciplinary Sciences (RIIS) at Parallelizing Stochastic Gradient Descent for Least Squares Regression which is why I created a dblp: Daogao Liu [pdf] [slides] Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan 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. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 This is the academic homepage of Yang Liu (I publish under Yang P. Liu). CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat With Cameron Musco and Christopher Musco. 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. I regularly advise Stanford students from a variety of departments. Aviv Tamar - Reinforcement Learning Research Labs - Technion aaron sidford cv We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Allen Liu - GitHub Pages Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games 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 . 475 Via Ortega Before attending Stanford, I graduated from MIT in May 2018. small tool to obtain upper bounds of such algebraic algorithms. SODA 2023: 4667-4767. [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. aaron sidford cv } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ [pdf] [talk] Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. However, even restarting can be a hard task here. with Arun Jambulapati, Aaron Sidford and Kevin Tian Their, This "Cited by" count includes citations to the following articles in Scholar. Secured intranet portal for faculty, staff and students. About - Annie Marsden with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Stanford, CA 94305 University, where 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Our method improves upon the convergence rate of previous state-of-the-art linear programming . dblp: Yin Tat Lee Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. with Yair Carmon, Arun Jambulapati and Aaron Sidford Faster Matroid Intersection Princeton University
Condos For Rent Moreno Valley, Volunteer Opportunities Oahu For High School Students, Misterio Quartz Pompeii, Articles A