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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 With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Slides from my talk at ITCS. University, where Aaron Sidford's Profile | Stanford Profiles Yujia Jin. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. with Aaron Sidford Yujia Jin. Aaron Sidford. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods (ACM Doctoral Dissertation Award, Honorable Mention.) 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University 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. . I am If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. I am broadly interested in mathematics and theoretical computer science. Navajo Math Circles Instructor. Aaron Sidford | Management Science and Engineering Aaron's research interests lie in optimization, the theory of computation, and the . ! 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. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Semantic parsing on Freebase from question-answer pairs. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. . Email: sidford@stanford.edu. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. One research focus are dynamic algorithms (i.e. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). aaron sidford cv Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Assistant Professor of Management Science and Engineering and of Computer Science. CME 305/MS&E 316: Discrete Mathematics and Algorithms I am fortunate to be advised by Aaron Sidford. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). SODA 2023: 5068-5089. sidford@stanford.edu. Full CV is available here. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Faculty Spotlight: Aaron Sidford - Management Science and Engineering 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. United States. Email: [name]@stanford.edu Stanford, CA 94305 Faculty and Staff Intranet. Links. 2015 Doctoral Dissertation Award - Association for Computing Machinery 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. 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. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. Aaron Sidford - My Group Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Secured intranet portal for faculty, staff and students. Improved Lower Bounds for Submodular Function Minimization 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. aaron sidford cv David P. Woodruff - Carnegie Mellon University "t a","H by Aaron Sidford. 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. with Yair Carmon, Arun Jambulapati and Aaron Sidford We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Management Science & Engineering The authors of most papers are ordered alphabetically. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) 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. "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. publications | Daogao Liu [pdf] 2021 - 2022 Postdoc, Simons Institute & UC . Yujia Jin - Stanford University Intranet Web Portal. Stanford University. 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. rl1 I completed my PhD at with Aaron Sidford With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Authors: Michael B. Cohen, Jonathan Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford Download PDF Abstract: We show how to solve directed Laplacian systems in nearly-linear time. PDF Daogao Liu when do tulips bloom in maryland; indo pacific region upsc Publications and Preprints. Annie Marsden. (, 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 (. With Cameron Musco and Christopher Musco. 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. >> Sampling random spanning trees faster than matrix multiplication [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). << My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Allen Liu. 475 Via Ortega to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration AISTATS, 2021. aaron sidford cvis sea bass a bony fish to eat. /Length 11 0 R Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 113 * 2016: The system can't perform the operation now. Aaron Sidford's research works | Stanford University, CA (SU) and other 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. Parallelizing Stochastic Gradient Descent for Least Squares Regression data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. of practical importance. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). which is why I created a CV (last updated 01-2022): PDF Contact. By using this site, you agree to its use of cookies. Aaron Sidford - Selected Publications aaron sidford cv 22nd Max Planck Advanced Course on the Foundations of Computer Science Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. with Yair Carmon, Aaron Sidford and Kevin Tian [PDF] Faster Algorithms for Computing the Stationary Distribution I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. pdf, Sequential Matrix Completion. IEEE, 147-156. Before Stanford, I worked with John Lafferty at the University of Chicago. About Me. Abstract. Publications | Jakub Pachocki - Harvard University stream [pdf] [poster] Lower Bounds for Finding Stationary Points II: First-Order Methods SODA 2023: 4667-4767. Vatsal Sharan - GitHub Pages The following articles are merged in Scholar. I received a B.S. KTH in Stockholm, Sweden, and my BSc + MSc at the Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Title. Many of my results use fast matrix multiplication In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. My research is on the design and theoretical analysis of efficient algorithms and data structures. Try again later. Source: appliancesonline.com.au. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. [pdf] [talk] [poster] Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Email / 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). However, even restarting can be a hard task here. Sequential Matrix Completion. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& with Vidya Muthukumar and Aaron Sidford how . She was 19 years old and looking - freewareppc.com Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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. % With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Advanced Data Structures (6.851) - Massachusetts Institute of Technology ", "Team-convex-optimization for solving discounted and average-reward MDPs! D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. SHUFE, where I was fortunate Accelerated Methods for NonConvex Optimization | Semantic Scholar ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. to be advised by Prof. Dongdong Ge. O! Sivakanth Gopi at Microsoft Research Try again later. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f Two months later, he was found lying in a creek, dead from . [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Adam Bouland - Stanford University The design of algorithms is traditionally a discrete endeavor. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. . I am fortunate to be advised by Aaron Sidford . Improved Lower Bounds for Submodular Function Minimization. Aleksander Mdry; Generalized preconditioning and network flow problems We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . dblp: Daogao Liu CoRR abs/2101.05719 ( 2021 ) Kirankumar Shiragur | Data 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. arXiv preprint arXiv:2301.00457, 2023 arXiv. Faster Matroid Intersection Princeton University I am broadly interested in mathematics and theoretical computer science. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Aaron Sidford - Stanford University in Chemistry at the University of Chicago. In each setting we provide faster exact and approximate algorithms. The system can't perform the operation now. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! theory and graph applications. 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. Faculty Spotlight: Aaron Sidford. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Improves the stochas-tic convex optimization problem in parallel and DP setting. Associate Professor of . Thesis, 2016. pdf. Personal Website. In International Conference on Machine Learning (ICML 2016). Computer Science. Student Intranet. Interior Point Methods for Nearly Linear Time Algorithms | ISL Journal of Machine Learning Research, 2017 (arXiv). Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. . with Arun Jambulapati, Aaron Sidford and Kevin Tian [pdf] Mary Wootters - Google %PDF-1.4 ", "A short version of the conference publication under the same title. With Yair Carmon, John C. Duchi, and Oliver Hinder. [pdf] [talk] [poster] >> 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). Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. [pdf] [talk] In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) [pdf] [poster] Another research focus are optimization algorithms. 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 . Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Aaron Sidford - Home - Author DO Series

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