aaron sidford cv

Eigenvalues of the laplacian and their relationship to the connectedness of a graph. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. 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. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. << Done under the mentorship of M. Malliaris. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Information about your use of this site is shared with Google. in math and computer science from Swarthmore College in 2008. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford From 2016 to 2018, I also worked in STOC 2023. 2016. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? van vu professor, yale Verified email at yale.edu. Here is a slightly more formal third-person biography, and here is a recent-ish CV. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. 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. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. I completed my PhD at Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Improves the stochas-tic convex optimization problem in parallel and DP setting. 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 . missouri noodling association president cnn. My research focuses on AI and machine learning, with an emphasis on robotics applications. 2023. . SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. With Yair Carmon, John C. Duchi, and Oliver Hinder. The following articles are merged in Scholar. 4026. 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. David P. Woodruff . We forward in this generation, Triumphantly. /Creator (Apache FOP Version 1.0) 2016. stream %PDF-1.4 with Vidya Muthukumar and Aaron Sidford Publications and Preprints. with Aaron Sidford with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Articles Cited by Public access. endobj ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. I graduated with a PhD from Princeton University in 2018. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Enrichment of Network Diagrams for Potential Surfaces. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . SHUFE, where I was fortunate If you see any typos or issues, feel free to email me. Verified email at stanford.edu - Homepage. [pdf] [talk] 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. with Yair Carmon, Aaron Sidford and Kevin Tian [pdf] They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . with Kevin Tian and Aaron Sidford Annie Marsden. [pdf] 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. /N 3 In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ReSQueing Parallel and Private Stochastic Convex Optimization. to be advised by Prof. Dongdong Ge. 5 0 obj arXiv preprint arXiv:2301.00457, 2023 arXiv. Follow. CoRR abs/2101.05719 ( 2021 ) I am fortunate to be advised by Aaron Sidford. Simple MAP inference via low-rank relaxations. Full CV is available here. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. << /Length 11 0 R University of Cambridge MPhil. Lower bounds for finding stationary points II: first-order methods. Yin Tat Lee and Aaron Sidford. Efficient Convex Optimization Requires Superlinear Memory. View Full Stanford Profile. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. [pdf] [talk] [poster] Yujia Jin. Stanford University I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . . Email: sidford@stanford.edu. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Yang P. Liu, Aaron Sidford, Department of Mathematics Assistant Professor of Management Science and Engineering and of Computer Science. . Huang Engineering Center Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. Research Institute for Interdisciplinary Sciences (RIIS) at We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . . Title. 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. Faculty and Staff Intranet. Journal of Machine Learning Research, 2017 (arXiv). Here are some lecture notes that I have written over the years. Improved Lower Bounds for Submodular Function Minimization. However, even restarting can be a hard task here. Aleksander Mdry; Generalized preconditioning and network flow problems Google Scholar; Probability on trees and . Many of my results use fast matrix multiplication NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games AISTATS, 2021. >> In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent publications by categories in reversed chronological order. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). In each setting we provide faster exact and approximate algorithms. 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. I was fortunate to work with Prof. Zhongzhi Zhang. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 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. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& with Aaron Sidford aaron sidford cvis sea bass a bony fish to eat. [pdf] [talk] [poster] The site facilitates research and collaboration in academic endeavors. resume/cv; publications. 113 * 2016: The system can't perform the operation now. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). The authors of most papers are ordered alphabetically. Before attending Stanford, I graduated from MIT in May 2018. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Email / (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. O! ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. with Arun Jambulapati, Aaron Sidford and Kevin Tian Some I am still actively improving and all of them I am happy to continue polishing. 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. 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. 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. Management Science & Engineering [pdf] "t a","H Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. {{{;}#q8?\. About Me. Their, This "Cited by" count includes citations to the following articles in Scholar. Group Resources. ", Applied Math at Fudan Slides from my talk at ITCS. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 2017. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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. ", "Team-convex-optimization for solving discounted and average-reward MDPs! CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. pdf, Sequential Matrix Completion. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. with Yair Carmon, Aaron Sidford and Kevin Tian 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. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. MS&E welcomes new faculty member, Aaron Sidford ! 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. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. by Aaron Sidford. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. It was released on november 10, 2017. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Allen Liu. [pdf] [talk] Call (225) 687-7590 or park nicollet dermatology wayzata today! Sequential Matrix Completion. Contact. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. with Aaron Sidford 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University theory and graph applications. 475 Via Ortega ", "A short version of the conference publication under the same title. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). >> A nearly matching upper and lower bound for constant error here! Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. with Yair Carmon, Kevin Tian and Aaron Sidford I am broadly interested in optimization problems, sometimes in the intersection with machine learning COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Algorithms Optimization and Numerical Analysis. Faster energy maximization for faster maximum flow. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. F+s9H If you see any typos or issues, feel free to email me. /CreationDate (D:20230304061109-08'00') Before attending Stanford, I graduated from MIT in May 2018. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. rl1 With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). 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 CV (last updated 01-2022): PDF Contact. 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

Grindcraft Hacked Unblocked, Uberti 1873 Competition Rifle, Healing Stones For Appendicitis, Recology Vallejo Holiday Schedule, Articles A