(ACM Doctoral Dissertation Award, Honorable Mention.) Mail Code. Aaron Sidford's research works | Stanford University, CA (SU) and other Aaron Sidford - Google Scholar /N 3 Allen Liu. In submission. Some I am still actively improving and all of them I am happy to continue polishing. Aaron Sidford - Home - Author DO Series We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. We forward in this generation, Triumphantly. Publications | Salil Vadhan publications by categories in reversed chronological order. Before attending Stanford, I graduated from MIT in May 2018. {{{;}#q8?\. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. [pdf] The design of algorithms is traditionally a discrete endeavor. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. 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 CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. O! 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. My CV. by Aaron Sidford. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. with Vidya Muthukumar and Aaron Sidford 2016. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. CME 305/MS&E 316: Discrete Mathematics and Algorithms It was released on november 10, 2017. Yujia Jin - Stanford University Aviv Tamar - Reinforcement Learning Research Labs - Technion University, Research Institute for Interdisciplinary Sciences (RIIS) at Summer 2022: I am currently a research scientist intern at DeepMind in London. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. ?_l) I am fortunate to be advised by Aaron Sidford. Before attending Stanford, I graduated from MIT in May 2018. Abstract. Aleksander Mdry; Generalized preconditioning and network flow problems Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. with Arun Jambulapati, Aaron Sidford and Kevin Tian My research focuses on AI and machine learning, with an emphasis on robotics applications. Applying this technique, we prove that any deterministic SFM algorithm . Aaron Sidford - Teaching Accelerated Methods for NonConvex Optimization | Semantic Scholar with Aaron Sidford stream [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Faculty Spotlight: Aaron Sidford - Management Science and Engineering AISTATS, 2021. Secured intranet portal for faculty, staff and students. 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. rl1 >> Links. Aaron Sidford | Management Science and Engineering Personal Website. Office: 380-T Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). with Aaron Sidford However, even restarting can be a hard task here. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. Annie Marsden. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. University of Cambridge MPhil. I received my PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where I was advised by Professor Jonathan Kelner. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. If you see any typos or issues, feel free to email me. 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. Improves the stochas-tic convex optimization problem in parallel and DP setting. Aaron Sidford. Group Resources. 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. Follow. which is why I created a ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . I completed my PhD at With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. /Creator (Apache FOP Version 1.0) International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods ", "Sample complexity for average-reward MDPs? with Yair Carmon, Kevin Tian and Aaron Sidford with Yair Carmon, Arun Jambulapati and Aaron Sidford Thesis, 2016. pdf. Sampling random spanning trees faster than matrix multiplication 2015 Doctoral Dissertation Award - Association for Computing Machinery Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. publications | Daogao Liu Management Science & Engineering Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Aaron Sidford - Selected Publications [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Aaron Sidford. how . aaron sidford cv In International Conference on Machine Learning (ICML 2016). ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Here is a slightly more formal third-person biography, and here is a recent-ish CV. F+s9H >> [pdf] [talk] [poster] Research Institute for Interdisciplinary Sciences (RIIS) at Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is . Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. 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. 9-21. 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. David P. Woodruff . Best Paper Award. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. 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. [pdf] [poster] The authors of most papers are ordered alphabetically. /Length 11 0 R Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). with Yair Carmon, Aaron Sidford and Kevin Tian 2021. [pdf] . Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . About Me. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG The following articles are merged in Scholar. "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. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). SODA 2023: 4667-4767. Enrichment of Network Diagrams for Potential Surfaces. aaron sidford cvis sea bass a bony fish to eat. I graduated with a PhD from Princeton University in 2018. Journal of Machine Learning Research, 2017 (arXiv). with Aaron Sidford >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. By using this site, you agree to its use of cookies. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. ReSQueing Parallel and Private Stochastic Convex Optimization. Yair Carmon. 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 . 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 Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. . Try again later. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Some I am still actively improving and all of them I am happy to continue polishing. 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. aaron sidford cvnatural fibrin removalnatural fibrin removal arXiv preprint arXiv:2301.00457, 2023 arXiv. 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. % The system can't perform the operation now. [pdf] Two months later, he was found lying in a creek, dead from . David P. Woodruff - Carnegie Mellon University With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. 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 . Alcatel flip phones are also ready to purchase with consumer cellular. [last name]@stanford.edu where [last name]=sidford. sidford@stanford.edu. I am With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Aaron Sidford - My Group MS&E213 / CS 269O - Introduction to Optimization Theory Advanced Data Structures (6.851) - Massachusetts Institute of Technology Nearly Optimal Communication and Query Complexity of Bipartite Matching . 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. Vatsal Sharan - GitHub Pages small tool to obtain upper bounds of such algebraic algorithms. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). resume/cv; publications. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y /CreationDate (D:20230304061109-08'00') ! theses are protected by copyright. 22nd Max Planck Advanced Course on the Foundations of Computer Science SHUFE, where I was fortunate . [pdf] This site uses cookies from Google to deliver its services and to analyze traffic. [pdf] ", Applied Math at Fudan D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. 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. In each setting we provide faster exact and approximate algorithms. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Simple MAP inference via low-rank relaxations. Google Scholar Digital Library; Russell Lyons and Yuval Peres. 2023. . Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Faster energy maximization for faster maximum flow. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Slides from my talk at ITCS. [pdf] [poster] Faculty Spotlight: Aaron Sidford. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu.
Bill Dawson Navy Seal,
Ross Livingstone Obituary,
Air Manager Alternative,
Used Ditch Witch For Sale,
Is Javin Hunter Married,
Articles A