aaron sidford cv

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. Anup B. Rao. 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. I am fortunate to be advised by Aaron Sidford. Aaron Sidford. 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 . endobj Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. My research is on the design and theoretical analysis of efficient algorithms and data structures. with Yair Carmon, Aaron Sidford and Kevin Tian Etude for the Park City Math Institute Undergraduate Summer School. Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova .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 Yang P. Liu and Aaron Sidford. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. University, Research Institute for Interdisciplinary Sciences (RIIS) at Yair Carmon. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 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 . [pdf] which is why I created a COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Email: [name]@stanford.edu In each setting we provide faster exact and approximate algorithms. 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. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). small tool to obtain upper bounds of such algebraic algorithms. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I am My interests are in the intersection of algorithms, statistics, optimization, and machine learning. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. by Aaron Sidford. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian [pdf] [slides] Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Vidya Muthukumar and Aaron Sidford Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. 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. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Applying this technique, we prove that any deterministic SFM algorithm . Two months later, he was found lying in a creek, dead from . 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. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Huang Engineering Center We forward in this generation, Triumphantly. However, many advances have come from a continuous viewpoint. I was fortunate to work with Prof. Zhongzhi Zhang. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. 2016. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation. 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 . The system can't perform the operation now. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. with Aaron Sidford The authors of most papers are ordered alphabetically. Nearly Optimal Communication and Query Complexity of Bipartite Matching . This site uses cookies from Google to deliver its services and to analyze traffic. SODA 2023: 5068-5089. I regularly advise Stanford students from a variety of departments. Yang P. Liu, Aaron Sidford, Department of Mathematics 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 113 * 2016: The system can't perform the operation now. (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. 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. Try again later. United States. /Producer (Apache FOP Version 1.0) with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Some I am still actively improving and all of them I am happy to continue polishing. 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] ", "A short version of the conference publication under the same title. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Management Science & Engineering In submission. Publications and Preprints. pdf, Sequential Matrix Completion. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . ", "Improved upper and lower bounds on first-order queries for solving $\min_{x}\max_{i\in[n]}\ell_i(x)$. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. /Filter /FlateDecode ! 2013. In International Conference on Machine Learning (ICML 2016). [pdf] 2021. I also completed my undergraduate degree (in mathematics) at MIT. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Faculty and Staff Intranet. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Enrichment of Network Diagrams for Potential Surfaces. theory and graph applications. with Aaron Sidford If you see any typos or issues, feel free to email me. About Me. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. 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 Assistant Professor of Management Science and Engineering and of Computer Science. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. I am fortunate to be advised by Aaron Sidford . ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. [pdf] With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ReSQueing Parallel and Private Stochastic Convex Optimization. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . missouri noodling association president cnn. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 4026. 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 Slides from my talk at ITCS. with Yair Carmon, Kevin Tian and Aaron Sidford 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 Cameron Musco and Christopher Musco. SODA 2023: 4667-4767. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. In Sidford's dissertation, Iterative Methods, Combinatorial . 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) Mail Code. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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 Jack Murtagh, Omer Reingold, and Salil P. Vadhan. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Before attending Stanford, I graduated from MIT in May 2018. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Np%p `a!2D4! AISTATS, 2021. [pdf] [poster] Group Resources. Many of my results use fast matrix multiplication Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. of practical importance. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA >> 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. ", "Sample complexity for average-reward MDPs? [last name]@stanford.edu where [last name]=sidford. % . Efficient Convex Optimization Requires Superlinear Memory. Alcatel flip phones are also ready to purchase with consumer cellular. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Before attending Stanford, I graduated from MIT in May 2018. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Their, This "Cited by" count includes citations to the following articles in Scholar. Here are some lecture notes that I have written over the years. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 2021 - 2022 Postdoc, Simons Institute & UC . Follow. Call (225) 687-7590 or park nicollet dermatology wayzata today! Done under the mentorship of M. Malliaris. I enjoy understanding the theoretical ground of many algorithms that are F+s9H "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. I am broadly interested in mathematics and theoretical computer science. ", "How many $\epsilon$-length segments do you need to look at for finding an $\epsilon$-optimal minimizer of convex function on a line? Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. David P. Woodruff . . In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) View Full Stanford Profile. [pdf] [poster] Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. [pdf] [talk] [poster] Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. My CV. 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. Title. publications by categories in reversed chronological order. If you see any typos or issues, feel free to email me. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. 2016. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs One research focus are dynamic algorithms (i.e. Student Intranet. Journal of Machine Learning Research, 2017 (arXiv). [pdf] O! My long term goal is to bring robots into human-centered domains such as homes and hospitals. 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. COLT, 2022. Secured intranet portal for faculty, staff and students. ", "We characterize when solving the max $\min_{x}\max_{i\in[n]}f_i(x)$ is (not) harder than solving the average $\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)$. sidford@stanford.edu. It was released on november 10, 2017. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Some I am still actively improving and all of them I am happy to continue polishing. [pdf] [talk] [pdf] [talk] [poster] Aaron Sidford Stanford University Verified email at stanford.edu. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . "t a","H arXiv | conference pdf, Annie Marsden, Sergio Bacallado. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. University of Cambridge MPhil. Here is a slightly more formal third-person biography, and here is a recent-ish CV. 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. AISTATS, 2021. Articles Cited by Public access. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Summer 2022: I am currently a research scientist intern at DeepMind in London. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in The following articles are merged in Scholar. With Yair Carmon, John C. Duchi, and Oliver Hinder. [pdf] [poster] This is the academic homepage of Yang Liu (I publish under Yang P. Liu). [pdf] [talk] Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Annie Marsden. 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. [pdf] [poster] ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. to be advised by Prof. Dongdong Ge. >>