By using this site, you agree to its use of cookies. View Full Stanford Profile. ! >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. 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 attending Stanford, I graduated from MIT in May 2018. 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 Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. sidford@stanford.edu. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. However, even restarting can be a hard task here. Efficient Convex Optimization Requires Superlinear Memory. I regularly advise Stanford students from a variety of departments. arXiv preprint arXiv:2301.00457, 2023 arXiv. in Mathematics and B.A. publications by categories in reversed chronological order. Enrichment of Network Diagrams for Potential Surfaces. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching >> In each setting we provide faster exact and approximate algorithms. Articles 1-20. MS&E welcomes new faculty member, Aaron Sidford ! Personal Website. [pdf] [talk] 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 . We also provide two . Aleksander Mdry; Generalized preconditioning and network flow problems Navajo Math Circles Instructor. 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. ?_l) I was fortunate to work with Prof. Zhongzhi Zhang. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y 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. (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, In Symposium on Foundations of Computer Science (FOCS 2015), Machtey Award for Best Student Paper (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv), Competing with the Empirical Risk Minimizer in a Single Pass, With Roy Frostig, Rong Ge, and Sham Kakade, In Conference on Learning Theory (COLT 2015) (arXiv), Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, In International Conference on Machine Learning (ICML 2015) (arXiv), Uniform Sampling for Matrix Approximation, With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng, In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2014), Best Paper Award and Machtey Award for Best Student Paper (arXiv), Single Pass Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco, An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations, With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia, In Symposium on Discrete Algorithms (SODA 2014), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv), A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time, With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu, In Symposium on the Theory of Computing (STOC 2013) (arXiv), SIAM Journal on Computing (arXiv before merge), Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space, With Jack Murtagh, Omer Reingold, and Salil Vadhan, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods. In this talk, I will present a new algorithm for solving linear programs. I also completed my undergraduate degree (in mathematics) at MIT. . [pdf] With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 of practical importance. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Annie Marsden. Student Intranet. with Yair Carmon, Kevin Tian and Aaron Sidford Computer Science. I enjoy understanding the theoretical ground of many algorithms that are with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford when do tulips bloom in maryland; indo pacific region upsc ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 with Aaron Sidford Selected recent papers . [pdf] [poster] with Yair Carmon, Arun Jambulapati and Aaron Sidford Try again later. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). The following articles are merged in Scholar. I am an Assistant Professor in the School of Computer Science at Georgia Tech. University of Cambridge MPhil. Goethe University in Frankfurt, Germany. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. [pdf] [talk] [poster] with Kevin Tian and Aaron Sidford D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. [pdf] [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. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 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, Nearly Optimal Communication and Query Complexity of Bipartite Matching . 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. Done under the mentorship of M. Malliaris. . Google Scholar; Probability on trees and . . with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford SHUFE, where I was fortunate Verified email at stanford.edu - Homepage. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Many of my results use fast matrix multiplication If you see any typos or issues, feel free to email me. 2016. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods /Filter /FlateDecode Journal of Machine Learning Research, 2017 (arXiv). Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 We forward in this generation, Triumphantly. 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 Stanford University Verified email at stanford.edu. 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 . ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. ICML, 2016. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. rl1 Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper 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. [pdf] to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration ", "Team-convex-optimization for solving discounted and average-reward MDPs! "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) I often do not respond to emails about applications. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. One research focus are dynamic algorithms (i.e. . ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Some I am still actively improving and all of them I am happy to continue polishing. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions STOC 2023. Assistant Professor of Management Science and Engineering and of Computer Science. in math and computer science from Swarthmore College in 2008. Two months later, he was found lying in a creek, dead from . My CV. 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. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 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. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. aaron sidford cvis sea bass a bony fish to eat. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. by Aaron Sidford. 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. 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. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Best Paper Award. 9-21. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Information about your use of this site is shared with Google. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Before attending Stanford, I graduated from MIT in May 2018. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 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 Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 2021 - 2022 Postdoc, Simons Institute & UC . Email / Yin Tat Lee and Aaron Sidford. I am broadly interested in mathematics and theoretical computer science. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. My interests are in the intersection of algorithms, statistics, optimization, and machine learning.
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