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 . SODA 2023: 5068-5089. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. University, Research Institute for Interdisciplinary Sciences (RIIS) at 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. I was fortunate to work with Prof. Zhongzhi Zhang. ?_l) with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian 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. I often do not respond to emails about applications. aaron sidford cvnatural fibrin removalnatural fibrin removal She was 19 years old and looking forward to the start of classes and reuniting with her college pals. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions of practical importance. Yin Tat Lee and Aaron Sidford. which is why I created a /CreationDate (D:20230304061109-08'00') Aaron's research interests lie in optimization, the theory of computation, and the . . Etude for the Park City Math Institute Undergraduate Summer School. Google Scholar Digital Library; Russell Lyons and Yuval Peres. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. 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 . 2016. Abstract. Yang P. Liu, Aaron Sidford, Department of Mathematics [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. pdf, Sequential Matrix Completion. 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 . riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA 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 If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Goethe University in Frankfurt, Germany. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. with Arun Jambulapati, Aaron Sidford and Kevin Tian 113 * 2016: The system can't perform the operation now. In Sidford's dissertation, Iterative Methods, Combinatorial . United States. Some I am still actively improving and all of them I am happy to continue polishing. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. %PDF-1.4 with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Stanford University with Yair Carmon, Aaron Sidford and Kevin Tian Huang Engineering Center 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 1 -Regression in Nearly Linear Time for Dense Instances. I also completed my undergraduate degree (in mathematics) at MIT. [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. << Before joining Stanford in Fall 2016, I was an NSF post-doctoral fellow at Carnegie Mellon University ; I received a Ph.D. in mathematics from the University of Michigan in 2014, and a B.A. " 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. Source: www.ebay.ie (ACM Doctoral Dissertation Award, Honorable Mention.) ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). Email: [name]@stanford.edu ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. SODA 2023: 4667-4767. The site facilitates research and collaboration in academic endeavors. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 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 Two months later, he was found lying in a creek, dead from . About Me. The system can't perform the operation now. 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 . "t a","H With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. << I received a B.S. 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. Best Paper Award. Np%p `a!2D4! Conference on Learning Theory (COLT), 2015. Here are some lecture notes that I have written over the years. ", "A short version of the conference publication under the same title. This site uses cookies from Google to deliver its services and to analyze traffic. 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. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. 2016. [pdf] [talk] Their, This "Cited by" count includes citations to the following articles in Scholar. rl1 ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? MS&E welcomes new faculty member, Aaron Sidford ! Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. CoRR abs/2101.05719 ( 2021 ) with Yair Carmon, Kevin Tian and Aaron Sidford Many of my results use fast matrix multiplication sidford@stanford.edu. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! KTH in Stockholm, Sweden, and my BSc + MSc at the with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford AISTATS, 2021. We also provide two . 4026. ReSQueing Parallel and Private Stochastic Convex Optimization. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration 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 . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. University of Cambridge MPhil. [pdf] My long term goal is to bring robots into human-centered domains such as homes and hospitals. I completed my PhD at I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Articles Cited by Public access. IEEE, 147-156. Articles 1-20. From 2016 to 2018, I also worked in I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. with Yair Carmon, Arun Jambulapati and Aaron Sidford One research focus are dynamic algorithms (i.e. Verified email at stanford.edu - Homepage. Faculty and Staff Intranet. We forward in this generation, Triumphantly. Full CV is available here. endobj Contact. Follow. Selected for oral presentation. Yujia Jin. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. resume/cv; publications. [pdf] ", "Sample complexity for average-reward MDPs? BayLearn, 2019, "Computing stationary solution for multi-agent RL is hard: Indeed, CCE for simultaneous games and NE for turn-based games are both PPAD-hard. STOC 2023. I am broadly interested in mathematics and theoretical computer science. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! F+s9H 2021 - 2022 Postdoc, Simons Institute & UC . A nearly matching upper and lower bound for constant error here! ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. COLT, 2022. [pdf] [poster] In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Personal Website. Google Scholar; Probability on trees and . ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. 2013. Before attending Stanford, I graduated from MIT in May 2018. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. 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. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. small tool to obtain upper bounds of such algebraic algorithms. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Information about your use of this site is shared with Google. [pdf] [talk] "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& CV (last updated 01-2022): PDF Contact. Allen Liu. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. However, even restarting can be a hard task here. 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] [talk] [poster] 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, with Yang P. Liu and Aaron Sidford. 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), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020).

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