[pdf] 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 D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A 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 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. Lower bounds for finding stationary points II: first-order methods. 4 0 obj 5 0 obj Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford 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. Journal of Machine Learning Research, 2017 (arXiv). 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. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. 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. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). with Yair Carmon, Arun Jambulapati and Aaron Sidford The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Aaron's research interests lie in optimization, the theory of computation, and the . With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Links. [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. 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. ?_l) 113 * 2016: The system can't perform the operation now. If you see any typos or issues, feel free to email me. Assistant Professor of Management Science and Engineering and of Computer Science. Algorithms Optimization and Numerical Analysis. Computer Science. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Office: 380-T Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. publications by categories in reversed chronological order. 2023. . [pdf] BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Annie Marsden. Here are some lecture notes that I have written over the years. University of Cambridge MPhil. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford 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). Goethe University in Frankfurt, Germany. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). F+s9H International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle I am broadly interested in mathematics and theoretical computer science. One research focus are dynamic algorithms (i.e. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). how . She was 19 years old and looking forward to the start of classes and reuniting with her college pals. STOC 2023. The design of algorithms is traditionally a discrete endeavor. 2016. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. 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 . David P. Woodruff . Conference on Learning Theory (COLT), 2015. << ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). sidford@stanford.edu. with Arun Jambulapati, Aaron Sidford and Kevin Tian In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Yang P. Liu, Aaron Sidford, Department of Mathematics ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . 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). missouri noodling association president cnn. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . to be advised by Prof. Dongdong Ge. 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. Title. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. [pdf] 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 . Before attending Stanford, I graduated from MIT in May 2018. The system can't perform the operation now. [pdf] [poster] This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& View Full Stanford Profile. Yujia Jin. Email: sidford@stanford.edu. Stanford, CA 94305 We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Navajo Math Circles Instructor. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. My research is on the design and theoretical analysis of efficient algorithms and data structures. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Articles Cited by Public access. pdf, Sequential Matrix Completion. Thesis, 2016. pdf. If you see any typos or issues, feel free to email me. The following articles are merged in Scholar. 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 . aaron sidford cvis sea bass a bony fish to eat. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! In each setting we provide faster exact and approximate algorithms. 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. . ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Secured intranet portal for faculty, staff and students. 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. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games We also provide two . 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. [pdf] [poster] NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games /N 3 ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. From 2016 to 2018, I also worked in Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. I am an Assistant Professor in the School of Computer Science at Georgia Tech. SODA 2023: 4667-4767. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. Here is a slightly more formal third-person biography, and here is a recent-ish CV. 475 Via Ortega [pdf] [talk] [poster] 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. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) when do tulips bloom in maryland; indo pacific region upsc ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. 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. Google Scholar; Probability on trees and . In this talk, I will present a new algorithm for solving linear programs. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). AISTATS, 2021. 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. Two months later, he was found lying in a creek, dead from . arXiv | conference pdf (alphabetical authorship) Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with . ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. 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 September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Neural Information Processing Systems (NeurIPS), 2014. by Aaron Sidford. << This site uses cookies from Google to deliver its services and to analyze traffic. rl1 The site facilitates research and collaboration in academic endeavors. aaron sidford cvnatural fibrin removalnatural fibrin removal 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. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Some I am still actively improving and all of them I am happy to continue polishing. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. with Aaron Sidford . Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . 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. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Research Institute for Interdisciplinary Sciences (RIIS) at Abstract. 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, They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . SODA 2023: 5068-5089. With Yair Carmon, John C. Duchi, and Oliver Hinder. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Etude for the Park City Math Institute Undergraduate Summer School. Anup B. Rao. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Alcatel flip phones are also ready to purchase with consumer cellular. [pdf] Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Personal Website. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. theses are protected by copyright. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 /Filter /FlateDecode Google Scholar Digital Library; Russell Lyons and Yuval Peres. Efficient Convex Optimization Requires Superlinear Memory. Associate Professor of . Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 July 8, 2022. 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 . Summer 2022: I am currently a research scientist intern at DeepMind in London. Sequential Matrix Completion. Improves the stochas-tic convex optimization problem in parallel and DP setting. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Follow. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. [pdf] I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. the Operations Research group. SHUFE, where I was fortunate Stanford University I am broadly interested in mathematics and theoretical computer science. small tool to obtain upper bounds of such algebraic algorithms. Done under the mentorship of M. Malliaris. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. United States. Full CV is available here. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. /CreationDate (D:20230304061109-08'00') Faculty and Staff Intranet. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization [pdf] Try again later. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Many of my results use fast matrix multiplication The authors of most papers are ordered alphabetically. I also completed my undergraduate degree (in mathematics) at MIT. with Aaron Sidford Articles 1-20. 2016. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. 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. {{{;}#q8?\. with Yair Carmon, Kevin Tian and Aaron Sidford 2017. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in 9-21. Email: [name]@stanford.edu Yin Tat Lee and Aaron Sidford. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss resume/cv; publications. with 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. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Email / en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. I was fortunate to work with Prof. Zhongzhi Zhang. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Secured intranet portal for faculty, staff and students. 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. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Best Paper Award. It was released on november 10, 2017. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. [pdf] [talk] van vu professor, yale Verified email at yale.edu. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Try again later. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Contact. 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. Selected recent papers . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Aaron Sidford. KTH in Stockholm, Sweden, and my BSc + MSc at the By using this site, you agree to its use of cookies. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Their, This "Cited by" count includes citations to the following articles in Scholar. Before attending Stanford, I graduated from MIT in May 2018. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. University, where We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Here are some lecture notes that I have written over the years. in Mathematics and B.A. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. in Chemistry at the University of Chicago. 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. 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. . /Creator (Apache FOP Version 1.0) Information about your use of this site is shared with Google.