Roy Frostig, Sida Wang, Percy Liang, Chris Manning. which is why I created a "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. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. % The design of algorithms is traditionally a discrete endeavor. Computer Science. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper 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. My CV. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. I regularly advise Stanford students from a variety of departments. rl1 With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). I am broadly interested in mathematics and theoretical computer science. SODA 2023: 4667-4767. [last name]@stanford.edu where [last name]=sidford. [pdf] [talk] [poster] Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. 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. Slides from my talk at ITCS. 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. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Email: [name]@stanford.edu with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford 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. (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. Information about your use of this site is shared with Google. About Me. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Email: sidford@stanford.edu. I also completed my undergraduate degree (in mathematics) at MIT. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. by Aaron Sidford. Call (225) 687-7590 or park nicollet dermatology wayzata today! how . Research Institute for Interdisciplinary Sciences (RIIS) at Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization 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. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. with Vidya Muthukumar and Aaron Sidford Yair Carmon. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 5 0 obj Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. 4026. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& 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). The site facilitates research and collaboration in academic endeavors. 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. Two months later, he was found lying in a creek, dead from . Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. [pdf] [poster] van vu professor, yale Verified email at yale.edu. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Simple MAP inference via low-rank relaxations. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. F+s9H We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . (, 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 (. to be advised by Prof. Dongdong Ge. Semantic parsing on Freebase from question-answer pairs. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. View Full Stanford Profile. Links. Google Scholar Digital Library; Russell Lyons and Yuval Peres. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). I graduated with a PhD from Princeton University in 2018. 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. [pdf] [talk] [poster] I enjoy understanding the theoretical ground of many algorithms that are In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Yin Tat Lee and Aaron Sidford. Np%p `a!2D4! in Mathematics and B.A. Stanford, CA 94305 We also provide two . 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. Try again later. We forward in this generation, Triumphantly. in math and computer science from Swarthmore College in 2008. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Yujia Jin. The following articles are merged in Scholar. In submission. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Their, This "Cited by" count includes citations to the following articles in Scholar. Verified email at stanford.edu - Homepage. 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. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Neural Information Processing Systems (NeurIPS), 2014. of practical importance. Publications and Preprints. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Faculty and Staff Intranet. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. . >> Try again later. sidford@stanford.edu. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. David P. Woodruff . It was released on november 10, 2017. Faculty Spotlight: Aaron Sidford. /Filter /FlateDecode [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Applying this technique, we prove that any deterministic SFM algorithm . . [pdf] [poster] Sequential Matrix Completion. Source: www.ebay.ie aaron sidford cvnatural fibrin removalnatural fibrin removal With Cameron Musco and Christopher Musco. The authors of most papers are ordered alphabetically. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. ", Applied Math at Fudan Summer 2022: I am currently a research scientist intern at DeepMind in London. with Yair Carmon, Kevin Tian and Aaron Sidford [pdf] stream Thesis, 2016. pdf. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 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 Lower bounds for finding stationary points II: first-order methods. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. with Yair Carmon, Aaron Sidford and Kevin Tian 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. Faster energy maximization for faster maximum flow. In this talk, I will present a new algorithm for solving linear programs. University of Cambridge MPhil. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. 9-21. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . However, many advances have come from a continuous viewpoint. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA [pdf] [poster] CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Anup B. Rao. 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 . Email / 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. (ACM Doctoral Dissertation Award, Honorable Mention.) In Sidford's dissertation, Iterative Methods, Combinatorial . ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. 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 . [pdf] Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian theory and graph applications. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). My research focuses on AI and machine learning, with an emphasis on robotics applications. Abstract. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Allen Liu. 2021 - 2022 Postdoc, Simons Institute & UC . Student Intranet. 2023. . Before attending Stanford, I graduated from MIT in May 2018. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. The system can't perform the operation now. I received a B.S. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . Secured intranet portal for faculty, staff and students. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. << Full CV is available here. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. One research focus are dynamic algorithms (i.e. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. ?_l) Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. ", "A short version of the conference publication under the same title. with Yang P. Liu and Aaron Sidford. Sidford received his PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner. I was fortunate to work with Prof. Zhongzhi Zhang. in Chemistry at the University of Chicago. 113 * 2016: The system can't perform the operation now. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ I am fortunate to be advised by Aaron Sidford. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Follow. aaron sidford cvis sea bass a bony fish to eat. Best Paper Award. Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. Contact. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. 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 [pdf] Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. 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. >> Articles Cited by Public access. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. with Yair Carmon, Arun Jambulapati and Aaron Sidford ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. 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. If you see any typos or issues, feel free to email me. In International Conference on Machine Learning (ICML 2016). Stanford University. [pdf] ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). A nearly matching upper and lower bound for constant error here! Before Stanford, I worked with John Lafferty at the University of Chicago. Aaron Sidford Stanford University Verified email at stanford.edu. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. /Producer (Apache FOP Version 1.0) University, where They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Enrichment of Network Diagrams for Potential Surfaces. I often do not respond to emails about applications. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Title. 475 Via Ortega ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. COLT, 2022. missouri noodling association president cnn. Goethe University in Frankfurt, Germany. Some I am still actively improving and all of them I am happy to continue polishing. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 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 . [pdf] Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. [pdf] [poster] KTH in Stockholm, Sweden, and my BSc + MSc at the I am broadly interested in mathematics and theoretical computer science. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. pdf, Sequential Matrix Completion. resume/cv; publications. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Improved Lower Bounds for Submodular Function Minimization. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle with Aaron Sidford BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Prof. Erik Demaine TAs: Timothy Kaler, Aaron Sidford [Home] [Assignments] [Open Problems] [Accessibility] sample frame from lecture videos Data structures play a central role in modern computer science. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford 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 Journal of Machine Learning Research, 2017 (arXiv). 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. [pdf] [talk] Articles 1-20. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. This site uses cookies from Google to deliver its services and to analyze traffic. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. with Yair Carmon, Aaron Sidford and Kevin Tian " 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. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ICML, 2016. Here are some lecture notes that I have written over the years. . 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.
