They will share a $10,000 prize, with financial sponsorship provided by Google Inc. with Yair Carmon, Aaron Sidford and Kevin Tian
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. Some I am still actively improving and all of them I am happy to continue polishing. Before Stanford, I worked with John Lafferty at the University of Chicago. My long term goal is to bring robots into human-centered domains such as homes and hospitals. PDF Daogao Liu with Aaron Sidford
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. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. 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. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods
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. aaron sidford cvnatural fibrin removalnatural fibrin removal
We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Links. Adam Bouland - Stanford University 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. 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. 5 0 obj This is the academic homepage of Yang Liu (I publish under Yang P. Liu). You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Journal of Machine Learning Research, 2017 (arXiv). (, 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 (. [last name]@stanford.edu where [last name]=sidford. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford.
[1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time AISTATS, 2021. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! van vu professor, yale Verified email at yale.edu. /Filter /FlateDecode xwXSsN`$!l{@ $@TR)XZ(
RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Yujia Jin. Stanford University. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ICML, 2016. . Publications | Jakub Pachocki - Harvard University Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f (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. aaron sidford cv natural fibrin removal - libiot.kku.ac.th
/N 3 Google Scholar, The Complexity of Infinite-Horizon General-Sum Stochastic Games, The Complexity of Optimizing Single and Multi-player Games, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions, On the Sample Complexity for Average-reward Markov Decision Processes, Stochastic Methods for Matrix Games and its Applications, Acceleration with a Ball Optimization Oracle, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, The Complexity of Infinite-Horizon General-Sum Stochastic Games
I am broadly interested in mathematics and theoretical computer science. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan
SODA 2023: 5068-5089.
Aaron Sidford receives best paper award at COLT 2022
Alcatel One Touch Flip Phone - New Product Recommendations, Promotions UGTCS Aaron Sidford's research works | Stanford University, CA (SU) and other 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 special case where variance reduction can be used to nonconvex optimization (monotone operators).
CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019.
Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. It was released on november 10, 2017. Google Scholar; Probability on trees and .
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I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. Aaron Sidford - My Group Semantic parsing on Freebase from question-answer pairs. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. "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. [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. With Cameron Musco and Christopher Musco. I am fortunate to be advised by Aaron Sidford. ", "Sample complexity for average-reward MDPs? 4 0 obj
Faculty Spotlight: Aaron Sidford. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games
Computer Science. 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. If you see any typos or issues, feel free to email me. View Full Stanford Profile. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. . what is a blind trust for lottery winnings; ithaca college park school scholarships; Aaron Sidford. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. 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 AISTATS, 2021. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Try again later. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. % Yujia Jin - Stanford University Best Paper Award.
Group Resources. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email).
I am broadly interested in mathematics and theoretical computer science. Lower Bounds for Finding Stationary Points II: First-Order Methods aaron sidford cv Articles Cited by Public access. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Simple MAP inference via low-rank relaxations.
With Yair Carmon, John C. Duchi, and Oliver Hinder. David P. Woodruff - Carnegie Mellon University Improved Lower Bounds for Submodular Function Minimization. Two months later, he was found lying in a creek, dead from . with Yair Carmon, Aaron Sidford and Kevin Tian
Anup B. Rao.
{{{;}#q8?\. Secured intranet portal for faculty, staff and students. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) with Aaron Sidford
Call (225) 687-7590 or park nicollet dermatology wayzata today! 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 . theory and graph applications. Gregory Valiant Homepage - Stanford University Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Sampling random spanning trees faster than matrix multiplication I enjoy understanding the theoretical ground of many algorithms that are
Yujia Jin. 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 . with Yair Carmon, Kevin Tian and Aaron Sidford
Source: www.ebay.ie 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 . This is the academic homepage of Yang Liu (I publish under Yang P. Liu). ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). My research is on the design and theoretical analysis of efficient algorithms and data structures. By using this site, you agree to its use of cookies. ReSQueing Parallel and Private Stochastic Convex Optimization. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Improved Lower Bounds for Submodular Function Minimization Navajo Math Circles Instructor. About - Annie Marsden [pdf]
in math and computer science from Swarthmore College in 2008. 2016. Mail Code. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. 113 * 2016: The system can't perform the operation now. 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 .
In each setting we provide faster exact and approximate algorithms. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in
Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
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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). Full CV is available here. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. dblp: Yin Tat Lee Contact. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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 Multiple Scales. Sivakanth Gopi at Microsoft Research ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. with Kevin Tian and Aaron Sidford
with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian
172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. I regularly advise Stanford students from a variety of departments. [pdf]
[pdf] [poster]
the Operations Research group. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). From 2016 to 2018, I also worked in
Management Science & Engineering With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. University, Research Institute for Interdisciplinary Sciences (RIIS) at
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arXiv preprint arXiv:2301.00457, 2023 arXiv. The system can't perform the operation now.
I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Aaron Sidford - Selected Publications I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Advanced Data Structures (6.851) - Massachusetts Institute of Technology
with Aaron Sidford
Iterative methods, combinatorial optimization, and linear programming with Yair Carmon, Aaron Sidford and Kevin Tian
CME 305/MS&E 316: Discrete Mathematics and Algorithms
Aaron Sidford - Google Scholar [pdf]
Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Eigenvalues of the laplacian and their relationship to the connectedness of a graph. [pdf] [poster]
. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
Associate Professor of . Aaron Sidford - Stanford University ", "Team-convex-optimization for solving discounted and average-reward MDPs! A nearly matching upper and lower bound for constant error here! I graduated with a PhD from Princeton University in 2018. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden.
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. 2013. 2021 - 2022 Postdoc, Simons Institute & UC . ?_l) With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. 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. of practical importance. " 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. pdf, Sequential Matrix Completion. 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.
[pdf] [talk] [poster]
SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. I was fortunate to work with Prof. Zhongzhi Zhang. SODA 2023: 4667-4767.
I am broadly interested in optimization problems, sometimes in the intersection with machine learning
Efficient Convex Optimization Requires Superlinear Memory. Yang P. Liu - GitHub Pages aaron sidford cv
I completed my PhD at
CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Stanford University
Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. 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. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms.