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", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Source: appliancesonline.com.au. Another research focus are optimization algorithms. Main Menu. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian with Yair Carmon, Aaron Sidford and Kevin Tian Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . endobj I am fortunate to be advised by Aaron Sidford. The site facilitates research and collaboration in academic endeavors. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Contact. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Annie Marsden. In submission. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Neural Information Processing Systems (NeurIPS), 2014. (ACM Doctoral Dissertation Award, Honorable Mention.) Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan 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 . by Aaron Sidford. . Their, This "Cited by" count includes citations to the following articles in Scholar. Yair Carmon. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Algorithms Optimization and Numerical Analysis. Semantic parsing on Freebase from question-answer pairs. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods I enjoy understanding the theoretical ground of many algorithms that are Stanford University AISTATS, 2021. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space 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. In this talk, I will present a new algorithm for solving linear programs. Aaron Sidford ([email protected]) 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. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. ReSQueing Parallel and Private Stochastic Convex Optimization. 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. If you see any typos or issues, feel free to email me. 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. In International Conference on Machine Learning (ICML 2016). Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Nearly Optimal Communication and Query Complexity of Bipartite Matching . ?_l) 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 . 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. ", "A short version of the conference publication under the same title. COLT, 2022. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. 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 Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. 2021 - 2022 Postdoc, Simons Institute & UC . Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Management Science & Engineering With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. << Yang P. Liu, Aaron Sidford, Department of Mathematics with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian SHUFE, where I was fortunate [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. Research Institute for Interdisciplinary Sciences (RIIS) at Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent /N 3 Student Intranet. resume/cv; publications. AISTATS, 2021. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. /Producer (Apache FOP Version 1.0) [pdf] [poster] Anup B. Rao. 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. ! pdf, Sequential Matrix Completion. [pdf] Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 2016. with Vidya Muthukumar and Aaron Sidford "t a","H I graduated with a PhD from Princeton University in 2018. Try again later. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . stream I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. O! They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Secured intranet portal for faculty, staff and students. University of Cambridge MPhil. in Mathematics and B.A. . Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. 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. 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, of practical importance. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 with Kevin Tian and Aaron Sidford Secured intranet portal for faculty, staff and students. [pdf] Before Stanford, I worked with John Lafferty at the University of Chicago. with Yair Carmon, Arun Jambulapati and Aaron Sidford My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 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 . 475 Via Ortega 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. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Follow. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. [pdf] [pdf] [poster] with Aaron Sidford [pdf] [talk] [poster] Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs with Yair Carmon, Arun Jambulapati and Aaron Sidford [pdf] [talk] [poster] With Yair Carmon, John C. Duchi, and Oliver Hinder. I am broadly interested in mathematics and theoretical computer science. 2021. MS&E welcomes new faculty member, Aaron Sidford ! We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. /CreationDate (D:20230304061109-08'00') Here are some lecture notes that I have written over the years. Stanford, CA 94305 SODA 2023: 4667-4767. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). By using this site, you agree to its use of cookies. From 2016 to 2018, I also worked in In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. << My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Improved Lower Bounds for Submodular Function Minimization. University, Research Institute for Interdisciplinary Sciences (RIIS) at 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 Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. small tool to obtain upper bounds of such algebraic algorithms. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. In each setting we provide faster exact and approximate algorithms. Assistant Professor of Management Science and Engineering and of Computer Science. Before attending Stanford, I graduated from MIT in May 2018. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. with Yang P. Liu and Aaron Sidford. Email / Done under the mentorship of M. Malliaris. Yujia Jin. The system can't perform the operation now. However, many advances have come from a continuous viewpoint. I am broadly interested in optimization problems, sometimes in the intersection with machine learning Email: [name]@stanford.edu Yin Tat Lee and Aaron Sidford. Stanford University. CV (last updated 01-2022): PDF Contact. 5 0 obj 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. The authors of most papers are ordered alphabetically. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). It was released on november 10, 2017. Aaron Sidford Stanford University Verified email at stanford.edu. publications by categories in reversed chronological order. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. My CV. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG in math and computer science from Swarthmore College in 2008. [pdf] [talk] 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. 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. 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. 2013. I regularly advise Stanford students from a variety of departments. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Full CV is available here. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper F+s9H 2023. . Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. CoRR abs/2101.05719 ( 2021 ) ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). My research is on the design and theoretical analysis of efficient algorithms and data structures. Simple MAP inference via low-rank relaxations. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. [pdf] [slides] with Aaron Sidford ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Thesis, 2016. pdf. I am July 8, 2022. I am fortunate to be advised by Aaron Sidford . United States. (, 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 (. Etude for the Park City Math Institute Undergraduate Summer School. ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. University, where [pdf] [poster] Huang Engineering Center Some I am still actively improving and all of them I am happy to continue polishing. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Np%p `a!2D4! Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. David P. Woodruff . To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Here are some lecture notes that I have written over the years. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Enrichment of Network Diagrams for Potential Surfaces. The design of algorithms is traditionally a discrete endeavor. Faster energy maximization for faster maximum flow.