Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. [pdf]
Before attending Stanford, I graduated from MIT in May 2018. the Operations Research group. with Kevin Tian and Aaron Sidford
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Bw!hz#0 )l`/8p.7p|O~ van vu professor, yale Verified email at yale.edu. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Improves the stochas-tic convex optimization problem in parallel and DP setting. when do tulips bloom in maryland; indo pacific region upsc 2023. . Aaron Sidford - All Publications
Aviv Tamar - Reinforcement Learning Research Labs - Technion I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 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. Alcatel One Touch Flip Phone - New Product Recommendations, Promotions dblp: Yin Tat Lee ", "Sample complexity for average-reward MDPs? 113 * 2016: The system can't perform the operation now. Research Institute for Interdisciplinary Sciences (RIIS) at
Many of my results use fast matrix multiplication
[last name]@stanford.edu where [last name]=sidford. Some I am still actively improving and all of them I am happy to continue polishing. 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. [pdf]
with Yair Carmon, Aaron Sidford and Kevin Tian
Faculty Spotlight: Aaron Sidford.
Conference on Learning Theory (COLT), 2015. 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.
Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Slides from my talk at ITCS.
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Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). I often do not respond to emails about applications. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Email: [name]@stanford.edu Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games
It was released on november 10, 2017. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration
22nd Max Planck Advanced Course on the Foundations of Computer Science
aaron sidford cv Faster energy maximization for faster maximum flow. %PDF-1.4 Efficient Convex Optimization Requires Superlinear Memory. Management Science & Engineering ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! 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. /Creator (Apache FOP Version 1.0) ! The site facilitates research and collaboration in academic endeavors. 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 . Aaron Sidford - Google Scholar 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. 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 . /Filter /FlateDecode Two months later, he was found lying in a creek, dead from .
One research focus are dynamic algorithms (i.e. Microsoft Research Faculty Fellowship 2020: Researchers in academia at International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG
Goethe University in Frankfurt, Germany. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Best Paper Award. 4 0 obj I am an Assistant Professor in the School of Computer Science at Georgia Tech. Huang Engineering Center ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? with Aaron Sidford
PDF Daogao Liu I am 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. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
I am broadly interested in mathematics and theoretical computer science. [pdf] [talk]
Navajo Math Circles Instructor. Lower bounds for finding stationary points II: first-order methods. I am broadly interested in optimization problems, sometimes in the intersection with machine learning
Np%p `a!2D4! We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. In International Conference on Machine Learning (ICML 2016).
[5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. In submission. From 2016 to 2018, I also worked in
Interior Point Methods for Nearly Linear Time Algorithms | ISL with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford
CoRR abs/2101.05719 ( 2021 ) MS&E213 / CS 269O - Introduction to Optimization Theory Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. [PDF] Faster Algorithms for Computing the Stationary Distribution The following articles are merged in Scholar. Links. with Yair Carmon, Arun Jambulapati and Aaron Sidford
Aaron Sidford - live-simons-institute.pantheon.berkeley.edu
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.
Google Scholar Digital Library; Russell Lyons and Yuval Peres. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Publications | Salil Vadhan in Chemistry at the University of Chicago. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. SODA 2023: 5068-5089. aaron sidford cvis sea bass a bony fish to eat. 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
They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Aaron Sidford - Stanford University With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. I am broadly interested in mathematics and theoretical computer science. xwXSsN`$!l{@ $@TR)XZ(
RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y 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 Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. >> With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Yujia Jin. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle
arXiv preprint arXiv:2301.00457, 2023 arXiv. Aaron Sidford. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. publications by categories in reversed chronological order. Source: appliancesonline.com.au. Applying this technique, we prove that any deterministic SFM algorithm . ", Applied Math at Fudan
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. 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. Mail Code. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. ?_l) The system can't perform the operation now. Faster Matroid Intersection Princeton University 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. . I also completed my undergraduate degree (in mathematics) at MIT. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. CME 305/MS&E 316: Discrete Mathematics and Algorithms Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. This site uses cookies from Google to deliver its services and to analyze traffic. University, Research Institute for Interdisciplinary Sciences (RIIS) at
To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. 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
Publications and Preprints.
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 in Mathematics and B.A. % riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Anup B. Rao - Google Scholar
IEEE, 147-156. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. missouri noodling association president cnn. Aaron Sidford - My Group (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. [pdf] [slides]
2021 - 2022 Postdoc, Simons Institute & UC . CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. However, many advances have come from a continuous viewpoint. Computer Science. Faculty Spotlight: Aaron Sidford - Management Science and Engineering D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Improved Lower Bounds for Submodular Function Minimization Jan van den Brand Aaron Sidford's Profile | Stanford Profiles ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods
Assistant Professor of Management Science and Engineering and of Computer Science. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford
If you see any typos or issues, feel free to email me. ", "A short version of the conference publication under the same title. View Full Stanford Profile. Aaron Sidford Stanford University Verified email at stanford.edu. /Producer (Apache FOP Version 1.0) Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)!
CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. Adam Bouland - Stanford University ReSQueing Parallel and Private Stochastic Convex Optimization. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Contact. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Main Menu.
In Symposium on Theory of Computing (STOC 2020) (arXiv), Constant Girth Approximation for Directed Graphs in Subquadratic Time, With Shiri Chechik, Yang P. Liu, and Omer Rotem, Leverage Score Sampling for Faster Accelerated Regression and ERM, With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli, In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv), Near-optimal Approximate Discrete and Continuous Submodular Function Minimization, In Symposium on Discrete Algorithms (SODA 2020) (arXiv), Fast and Space Efficient Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos, In Conference on Neural Information Processing Systems (NeurIPS 2019), Complexity of Highly Parallel Non-Smooth Convex Optimization, With Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, A Direct (1/) Iteration Parallel Algorithm for Optimal Transport, In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv), A General Framework for Efficient Symmetric Property Estimation, With Moses Charikar and Kirankumar Shiragur, Parallel Reachability in Almost Linear Work and Square Root Depth, In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv), With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong, Deterministic Approximation of Random Walks in Small Space, With Jack Murtagh, Omer Reingold, and Salil P. Vadhan, In International Workshop on Randomization and Computation (RANDOM 2019), A Rank-1 Sketch for Matrix Multiplicative Weights, With Yair Carmon, John C. Duchi, and Kevin Tian, In Conference on Learning Theory (COLT 2019) (arXiv), Near-optimal method for highly smooth convex optimization, Efficient profile maximum likelihood for universal symmetric property estimation, In Symposium on Theory of Computing (STOC 2019) (arXiv), Memory-sample tradeoffs for linear regression with small error, Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications, With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi, In Symposium on Discrete Algorithms (SODA 2019) (arXiv), Exploiting Numerical Sparsity for Efficient Learning: Faster Eigenvector Computation and Regression, In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv), Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model, With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye, Coordinate Methods for Accelerating Regression and Faster Approximate Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2018), Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations, With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao, In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv), Efficient Convex Optimization with Membership Oracles, In Conference on Learning Theory (COLT 2018) (arXiv), Accelerating Stochastic Gradient Descent for Least Squares Regression, With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli, Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners.
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