Done under the mentorship of M. Malliaris. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Roy Frostig - Stanford University Simple MAP inference via low-rank relaxations. in Mathematics and B.A. aaron sidford cv natural fibrin removal - libiot.kku.ac.th I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. ICML, 2016. Advanced Data Structures (6.851) - Massachusetts Institute of Technology 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. Abstract. 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 . Thesis, 2016. pdf. IEEE, 147-156. (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. Selected for oral presentation. 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. with Aaron Sidford We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration COLT, 2022. 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 . Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Anup B. Rao. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Student Intranet. Faster energy maximization for faster maximum flow. Anup B. Rao - Google Scholar July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. . 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. [pdf] I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. I am broadly interested in mathematics and theoretical computer science. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. /Producer (Apache FOP Version 1.0) with Yair Carmon, Arun Jambulapati and Aaron Sidford Source: appliancesonline.com.au. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . Etude for the Park City Math Institute Undergraduate Summer School. Research Institute for Interdisciplinary Sciences (RIIS) at Mail Code. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Try again later. Aaron Sidford - My Group ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. By using this site, you agree to its use of cookies. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. 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. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). /CreationDate (D:20230304061109-08'00') I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. 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. to be advised by Prof. Dongdong Ge. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. [pdf] [poster] 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 fortunate to be advised by Aaron Sidford . [pdf] [talk] [poster] Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Sampling random spanning trees faster than matrix multiplication UGTCS Before attending Stanford, I graduated from MIT in May 2018. AISTATS, 2021. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Aaron Sidford Alcatel One Touch Flip Phone - New Product Recommendations, Promotions CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. About - Annie Marsden 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). ", "Team-convex-optimization for solving discounted and average-reward MDPs! Annie Marsden. Before attending Stanford, I graduated from MIT in May 2018. I am Interior Point Methods for Nearly Linear Time Algorithms | ISL 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. Np%p `a!2D4! Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. aaron sidford cvnatural fibrin removalnatural fibrin removal In submission. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Intranet Web Portal. Goethe University in Frankfurt, Germany. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Allen Liu. 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. Full CV is available here. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Yair Carmon. However, even restarting can be a hard task here. theory and graph applications. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs With Cameron Musco and Christopher Musco. Publications | Salil Vadhan Articles Cited by Public access. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? SODA 2023: 4667-4767. Aaron Sidford receives best paper award at COLT 2022 Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss The following articles are merged in Scholar. CV (last updated 01-2022): PDF Contact. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). Gregory Valiant Homepage - Stanford University Navajo Math Circles Instructor. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Sivakanth Gopi at Microsoft Research with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. I enjoy understanding the theoretical ground of many algorithms that are In Sidford's dissertation, Iterative Methods, Combinatorial . Google Scholar Digital Library; Russell Lyons and Yuval Peres. Faculty Spotlight: Aaron Sidford - Management Science and Engineering which is why I created a Vatsal Sharan - GitHub Pages Aaron Sidford - Stanford University Aaron Sidford - Home - Author DO Series 475 Via Ortega Assistant Professor of Management Science and Engineering and of Computer Science. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games (ACM Doctoral Dissertation Award, Honorable Mention.) Office: 380-T With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. in Chemistry at the University of Chicago. 2015 Doctoral Dissertation Award - Association for Computing Machinery David P. Woodruff - Carnegie Mellon University Publications | Jakub Pachocki - Harvard University Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 aaron sidford cv rl1 Follow. Neural Information Processing Systems (NeurIPS), 2014. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I received a B.S. Jan van den Brand With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. CME 305/MS&E 316: Discrete Mathematics and Algorithms Google Scholar; Probability on trees and . Improved Lower Bounds for Submodular Function Minimization. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). "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. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. publications by categories in reversed chronological order. 2022 - Learning and Games Program, Simons Institute, Sept. 2021 - Young Researcher Workshop, Cornell ORIE, Sept. 2021 - ACO Student Seminar, Georgia Tech, Dec. 2019 - NeurIPS Spotlight presentation. the Operations Research group. with Aaron Sidford 2021 - 2022 Postdoc, Simons Institute & UC . Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Aaron Sidford | Stanford Online with Vidya Muthukumar and Aaron Sidford We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Algorithms Optimization and Numerical Analysis. [pdf] with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Conference on Learning Theory (COLT), 2015. 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. SODA 2023: 5068-5089. 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 . ?_l) We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Our method improves upon the convergence rate of previous state-of-the-art linear programming . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. with Yang P. Liu and Aaron Sidford. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Yang P. Liu, Aaron Sidford, Department of Mathematics Yang P. Liu - GitHub Pages Enrichment of Network Diagrams for Potential Surfaces. Two months later, he was found lying in a creek, dead from . Some I am still actively improving and all of them I am happy to continue polishing. Aaron Sidford - Teaching Computer Science. Aaron Sidford - Google Scholar ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). [pdf] [talk] Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. We also provide two . Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA %PDF-1.4 [pdf] [talk] [poster] Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. by Aaron Sidford. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Management Science & Engineering Title. In this talk, I will present a new algorithm for solving linear programs. Source: www.ebay.ie Personal Website. Microsoft Research Faculty Fellowship 2020: Researchers in academia at 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! 2013. arXiv preprint arXiv:2301.00457, 2023 arXiv. Aleksander Mdry; Generalized preconditioning and network flow problems Faster Matroid Intersection Princeton University This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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. Semantic parsing on Freebase from question-answer pairs. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Iterative methods, combinatorial optimization, and linear programming My research is on the design and theoretical analysis of efficient algorithms and data structures. 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, 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. 2023. . PDF Daogao Liu Contact. I am an Assistant Professor in the School of Computer Science at Georgia Tech. 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 We forward in this generation, Triumphantly. Group Resources. Applying this technique, we prove that any deterministic SFM algorithm . 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. ", "Sample complexity for average-reward MDPs? of practical importance. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Aaron Sidford. STOC 2023. 2016. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. I am broadly interested in mathematics and theoretical computer science. sidford@stanford.edu. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! when do tulips bloom in maryland; indo pacific region upsc 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.