Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." This is the academic homepage of Yang Liu (I publish under Yang P. Liu). >>
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Summer 2022: I am currently a research scientist intern at DeepMind in London. 4026. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle
A nearly matching upper and lower bound for constant error here! Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA
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 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. Source: www.ebay.ie Etude for the Park City Math Institute Undergraduate Summer School. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Journal of Machine Learning Research, 2017 (arXiv). Management Science & Engineering how . [pdf]
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AISTATS, 2021. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods
I am an Assistant Professor in the School of Computer Science at Georgia Tech. Their, This "Cited by" count includes citations to the following articles in Scholar. Anup B. Rao. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching
Two months later, he was found lying in a creek, dead from .
", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Email: sidford@stanford.edu. My research focuses on AI and machine learning, with an emphasis on robotics applications. 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. In each setting we provide faster exact and approximate algorithms. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. Slides from my talk at ITCS. Annie Marsden. IEEE, 147-156. 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, 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 . Intranet Web Portal.
If you see any typos or issues, feel free to email me. with Yair Carmon, Aaron Sidford and Kevin Tian
Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. 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. "FV %H"Hr
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c0 L& 9cX& %PDF-1.4 In International Conference on Machine Learning (ICML 2016). Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford.
ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games
Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. F+s9H Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. [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. I graduated with a PhD from Princeton University in 2018. ReSQueing Parallel and Private Stochastic Convex Optimization. (, 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 (. Aaron Sidford. 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. theory and graph applications. Applying this technique, we prove that any deterministic SFM algorithm .
Source: appliancesonline.com.au. which is why I created a
Selected for oral presentation. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Navajo Math Circles Instructor.
I completed my PhD at
Aaron's research interests lie in optimization, the theory of computation, and the . Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory.
rl1 In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Yang P. Liu, Aaron Sidford, Department of Mathematics Alcatel flip phones are also ready to purchase with consumer cellular. I enjoy understanding the theoretical ground of many algorithms that are
Assistant Professor of Management Science and Engineering and of Computer Science.
Faculty and Staff Intranet. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Abstract. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. We forward in this generation, Triumphantly. Neural Information Processing Systems (NeurIPS), 2014. In submission. 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. 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. 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 . Yujia Jin. 4 0 obj 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. Contact. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. /Filter /FlateDecode Links. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . 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 . Before attending Stanford, I graduated from MIT in May 2018. About Me. If you see any typos or issues, feel free to email me.
in Chemistry at the University of Chicago. Here are some lecture notes that I have written over the years. . July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Faster energy maximization for faster maximum flow. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 with Aaron Sidford
", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). (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. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent
Improved Lower Bounds for Submodular Function Minimization. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization.
"t a","H Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. with Aaron Sidford
Page 1 of 5 Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science CONTACT INFORMATION Administrative Contact Jackie Nguyen - Administrative Associate
Student Intranet. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 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. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. aaron sidford cvnatural fibrin removalnatural fibrin removal 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). Enrichment of Network Diagrams for Potential Surfaces. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Secured intranet portal for faculty, staff and students. United States. } 4(JR!$AkRf[(t
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Follow. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. " 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. My CV. [pdf]
SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. The design of algorithms is traditionally a discrete endeavor. SHUFE, where I was fortunate
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. [pdf]
/Creator (Apache FOP Version 1.0) 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 . Main Menu. Semantic parsing on Freebase from question-answer pairs. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Computer Science. View Full Stanford Profile. resume/cv; publications.
", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. pdf, Sequential Matrix Completion. Lower bounds for finding stationary points II: first-order methods. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 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. COLT, 2022. This site uses cookies from Google to deliver its services and to analyze traffic. 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 . NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games
With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. [pdf] [talk] [poster]
. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford
publications by categories in reversed chronological order. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. 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. 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. 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.
We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. 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 with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian
>> Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. .
Group Resources. Try again later.
", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. ", "Team-convex-optimization for solving discounted and average-reward MDPs!
[pdf] [poster]
I was fortunate to work with Prof. Zhongzhi Zhang. what is a blind trust for lottery winnings; ithaca college park school scholarships;
Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper of practical importance. Stanford University. Selected recent papers . D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. [pdf] [talk] [poster]
There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. sidford@stanford.edu. 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. theses are protected by copyright. ICML, 2016. Full CV is available here. SODA 2023: 5068-5089. From 2016 to 2018, I also worked in
However, even restarting can be a hard task here.
With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. MS&E welcomes new faculty member, Aaron Sidford ! I am broadly interested in mathematics and theoretical computer science. 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. My long term goal is to bring robots into human-centered domains such as homes and hospitals. Yin Tat Lee and Aaron Sidford. 2021 - 2022 Postdoc, Simons Institute & UC . [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Information about your use of this site is shared with Google. Articles Cited by Public access. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Publications and Preprints. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Improves the stochas-tic convex optimization problem in parallel and DP setting. with Aaron Sidford
with Arun Jambulapati, Aaron Sidford and Kevin Tian
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 ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . Another research focus are optimization algorithms.
I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. I am a senior researcher in the Algorithms group at Microsoft Research Redmond.
Yujia Jin. van vu professor, yale Verified email at yale.edu. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. One research focus are dynamic algorithms (i.e. [pdf]
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.
small tool to obtain upper bounds of such algebraic algorithms. stream 475 Via Ortega Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Some I am still actively improving and all of them I am happy to continue polishing. In this talk, I will present a new algorithm for solving linear programs. 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. 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.
Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions
Before attending Stanford, I graduated from MIT in May 2018.
It was released on november 10, 2017. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 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. 2016. Best Paper Award. % ", Applied Math at Fudan
International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
CV (last updated 01-2022): PDF Contact. with Yair Carmon, Aaron Sidford and Kevin Tian
9-21. in math and computer science from Swarthmore College in 2008. Simple MAP inference via low-rank relaxations.
Email: [name]@stanford.edu Call (225) 687-7590 or park nicollet dermatology wayzata today! The following articles are merged in Scholar. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. 2013. Yair Carmon. << when do tulips bloom in maryland; indo pacific region upsc I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Before Stanford, I worked with John Lafferty at the University of Chicago. Np%p `a!2D4! {{{;}#q8?\.
172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second .
", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory.
endobj 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
However, many advances have come from a continuous viewpoint. 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 am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. 2017. I am
Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )!
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2012 Chevy Sonic Temperature Sensor Location, Blue Star Ointment On Acne, Fallout: New Vegas Unlock All Achievements Command, Articles A