<< Some I am still actively improving and all of them I am happy to continue polishing. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Verified email at stanford.edu - Homepage. with Yair Carmon, Arun Jambulapati and Aaron Sidford The design of algorithms is traditionally a discrete endeavor. United States. PDF Daogao Liu data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Mary Wootters - Google 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 graduated with a PhD from Princeton University in 2018. 9-21. Title. Yang P. Liu, Aaron Sidford, Department of Mathematics Try again later. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. " 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. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Aaron Sidford - All Publications I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. The paper, Efficient Convex Optimization Requires Superlinear Memory, was co-authored with Stanford professor Gregory Valiant as well as current Stanford student Annie Marsden and alumnus Vatsal Sharan. Aviv Tamar - Reinforcement Learning Research Labs - Technion Aaron Sidford - My Group [pdf] [talk] in Chemistry at the University of Chicago. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. SODA 2023: 5068-5089. Nearly Optimal Communication and Query Complexity of Bipartite Matching . with Kevin Tian and Aaron Sidford My research focuses on AI and machine learning, with an emphasis on robotics applications. Simple MAP inference via low-rank relaxations. Before Stanford, I worked with John Lafferty at the University of Chicago. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . theory and graph applications. . With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Summer 2022: I am currently a research scientist intern at DeepMind in London. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Sivakanth Gopi at Microsoft Research She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Email: [email protected]. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Group Resources. Microsoft Research Faculty Fellowship 2020: Researchers in academia at } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. (, 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 (. Links. . /N 3 Lower bounds for finding stationary points II: first-order methods. with Yair Carmon, Aaron Sidford and Kevin Tian You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Student Intranet. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Jan van den Brand He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. Thesis, 2016. pdf. Efficient Convex Optimization Requires Superlinear Memory. With Yair Carmon, John C. Duchi, and Oliver Hinder. 2023. . AISTATS, 2021. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . About Me. dblp: Yin Tat Lee Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . [last name]@stanford.edu where [last name]=sidford. 2016. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian ?_l) My long term goal is to bring robots into human-centered domains such as homes and hospitals. ", "Sample complexity for average-reward MDPs? [pdf] Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Another research focus are optimization algorithms. 113 * 2016: The system can't perform the operation now. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Faculty and Staff Intranet. endobj My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Many of my results use fast matrix multiplication Best Paper Award. KTH in Stockholm, Sweden, and my BSc + MSc at the In submission. Full CV is available here. 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. [pdf] [PDF] Faster Algorithms for Computing the Stationary Distribution Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! I am broadly interested in mathematics and theoretical computer science. Stanford University Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. I was fortunate to work with Prof. Zhongzhi Zhang. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. One research focus are dynamic algorithms (i.e. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. Aaron Sidford's research works | Stanford University, CA (SU) and other with Aaron Sidford July 8, 2022. This site uses cookies from Google to deliver its services and to analyze traffic. I completed my PhD at I hope you enjoy the content as much as I enjoyed teaching the class and if you have questions or feedback on the note, feel free to email me. pdf, Sequential Matrix Completion. Semantic parsing on Freebase from question-answer pairs. AISTATS, 2021. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). . In each setting we provide faster exact and approximate algorithms. when do tulips bloom in maryland; indo pacific region upsc . Aaron Sidford - Selected Publications 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 ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Faster energy maximization for faster maximum flow. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Here are some lecture notes that I have written over the years. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Allen Liu. Adam Bouland - Stanford University My CV. The following articles are merged in Scholar. Applying this technique, we prove that any deterministic SFM algorithm . Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 With Cameron Musco and Christopher Musco. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Lower Bounds for Finding Stationary Points II: First-Order Methods I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. In Sidford's dissertation, Iterative Methods, Combinatorial . A Faster Algorithm for Linear Programming and the Maximum Flow Problem II Accelerated Methods for NonConvex Optimization | Semantic Scholar 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 . Faculty Spotlight: Aaron Sidford - Management Science and Engineering 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. [pdf] [poster] MS&E welcomes new faculty member, Aaron Sidford ! Improves the stochas-tic convex optimization problem in parallel and DP setting. Slides from my talk at ITCS. Selected recent papers . in math and computer science from Swarthmore College in 2008. Np%p `a!2D4! 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 Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) 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. 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 . (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. 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. 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. 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. Aaron Sidford - Stanford University In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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). Iterative methods, combinatorial optimization, and linear programming van vu professor, yale Verified email at yale.edu. Sampling random spanning trees faster than matrix multiplication aaron sidford cv Follow. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games CV (last updated 01-2022): PDF Contact. with Aaron Sidford About - Annie Marsden 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 ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! Source: www.ebay.ie Some I am still actively improving and all of them I am happy to continue polishing. David P. Woodruff . with Aaron Sidford Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Secured intranet portal for faculty, staff and students. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 [pdf] Journal of Machine Learning Research, 2017 (arXiv). Annie Marsden. /Producer (Apache FOP Version 1.0) Publications | Jakub Pachocki - Harvard University %PDF-1.4 Aaron Sidford Stanford University Verified email at stanford.edu. Goethe University in Frankfurt, Germany. 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. Aleksander Mdry; Generalized preconditioning and network flow problems Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper I often do not respond to emails about applications.
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