aaron sidford cv

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. aaron sidford cv [pdf] Email / KTH in Stockholm, Sweden, and my BSc + MSc at the Yin Tat Lee and Aaron Sidford. University, Research Institute for Interdisciplinary Sciences (RIIS) at We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. Research Institute for Interdisciplinary Sciences (RIIS) at Our method improves upon the convergence rate of previous state-of-the-art linear programming . Interior Point Methods for Nearly Linear Time Algorithms | ISL Follow. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Yujia Jin. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Aviv Tamar - Reinforcement Learning Research Labs - Technion Parallelizing Stochastic Gradient Descent for Least Squares Regression Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Stanford University. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs The system can't perform the operation now. Management Science & Engineering A nearly matching upper and lower bound for constant error here! Another research focus are optimization algorithms. From 2016 to 2018, I also worked in Enrichment of Network Diagrams for Potential Surfaces. Semantic parsing on Freebase from question-answer pairs. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Aaron Sidford - My Group with Aaron Sidford I am a senior researcher in the Algorithms group at Microsoft Research Redmond. " 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. I regularly advise Stanford students from a variety of departments. Sivakanth Gopi at Microsoft Research Journal of Machine Learning Research, 2017 (arXiv). 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. Abstract. COLT, 2022. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games AISTATS, 2021. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Title. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Kirankumar Shiragur | Data Science Aaron Sidford - Selected Publications to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. If you see any typos or issues, feel free to email me. The authors of most papers are ordered alphabetically. ?_l) However, even restarting can be a hard task here. 113 * 2016: The system can't perform the operation now. 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. [pdf] Aaron Sidford - Google Scholar There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. rl1 I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 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. Goethe University in Frankfurt, Germany. with Yair Carmon, Aaron Sidford and Kevin Tian 4026. with Aaron Sidford With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. With Cameron Musco and Christopher Musco. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! Publications and Preprints. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games in Chemistry at the University of Chicago. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Simple MAP inference via low-rank relaxations. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. [pdf] [poster] With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." In this talk, I will present a new algorithm for solving linear programs. Aleksander Mdry; Generalized preconditioning and network flow problems Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Here are some lecture notes that I have written over the years. Before Stanford, I worked with John Lafferty at the University of Chicago. I often do not respond to emails about applications. Links. aaron sidford cv Secured intranet portal for faculty, staff and students. with Yair Carmon, Arun Jambulapati and Aaron Sidford We forward in this generation, Triumphantly. I was fortunate to work with Prof. Zhongzhi Zhang. . 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. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Verified email at stanford.edu - Homepage. Improves the stochas-tic convex optimization problem in parallel and DP setting. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods 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. Etude for the Park City Math Institute Undergraduate Summer School. Best Paper Award. With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Navajo Math Circles Instructor. 22nd Max Planck Advanced Course on the Foundations of Computer Science Faculty Spotlight: Aaron Sidford. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian In each setting we provide faster exact and approximate algorithms. I am broadly interested in optimization problems, sometimes in the intersection with machine learning ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Yang P. Liu, Aaron Sidford, Department of Mathematics 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. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). . Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Yujia Jin. with Aaron Sidford CV (last updated 01-2022): PDF Contact. Some I am still actively improving and all of them I am happy to continue polishing. AISTATS, 2021. MS&E welcomes new faculty member, Aaron Sidford ! 4 0 obj In Sidford's dissertation, Iterative Methods, Combinatorial . 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 Before attending Stanford, I graduated from MIT in May 2018. Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. 5 0 obj Before attending Stanford, I graduated from MIT in May 2018. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 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 Np%p `a!2D4! I am broadly interested in mathematics and theoretical computer science. I am an Assistant Professor in the School of Computer Science at Georgia Tech. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). The design of algorithms is traditionally a discrete endeavor. I enjoy understanding the theoretical ground of many algorithms that are I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Roy Frostig - Stanford University Source: www.ebay.ie 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 . CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Source: appliancesonline.com.au. % Adam Bouland - Stanford University In Symposium on Foundations of Computer Science (FOCS 2017) (arXiv), "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, With Yair Carmon, John C. Duchi, and Oliver Hinder, In International Conference on Machine Learning (ICML 2017) (arXiv), Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, and, Adrian Vladu, In Symposium on Theory of Computing (STOC 2017), Subquadratic Submodular Function Minimization, With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong, In Symposium on Theory of Computing (STOC 2017) (arXiv), Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, and Adrian Vladu, In Symposium on Foundations of Computer Science (FOCS 2016) (arXiv), With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki, In Symposium on Theory of Computing (STOC 2016) (arXiv), With Alina Ene, Gary L. Miller, and Jakub Pachocki, Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm, With Prateek Jain, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli, In Conference on Learning Theory (COLT 2016) (arXiv), Principal Component Projection Without Principal Component Analysis, With Roy Frostig, Cameron Musco, and Christopher Musco, In International Conference on Machine Learning (ICML 2016) (arXiv), Faster Eigenvector Computation via Shift-and-Invert Preconditioning, With Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, and Praneeth Netrapalli, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. July 8, 2022. Aaron Sidford - Teaching Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Gregory Valiant Homepage - Stanford University David P. Woodruff . 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University [pdf] xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Articles 1-20. 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. ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? ", "Sample complexity for average-reward MDPs? I am broadly interested in mathematics and theoretical computer science. /Length 11 0 R Huang Engineering Center 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). 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. when do tulips bloom in maryland; indo pacific region upsc Sequential Matrix Completion. Aaron's research interests lie in optimization, the theory of computation, and the . missouri noodling association president cnn. aaron sidford cvis sea bass a bony fish to eat. Iterative methods, combinatorial optimization, and linear programming Email: sidford@stanford.edu. with Yair Carmon, Aaron Sidford and Kevin Tian . {{{;}#q8?\. If you see any typos or issues, feel free to email me. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 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 . theses are protected by copyright. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. [PDF] Faster Algorithms for Computing the Stationary Distribution ", Applied Math at Fudan CME 305/MS&E 316: Discrete Mathematics and Algorithms 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 In submission. July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Aaron Sidford | Management Science and Engineering About - Annie Marsden Yang P. Liu - GitHub Pages 2017. 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. Who Does Shoshanna Get Engaged To, Felicity Vuolo Syndrome, Dixie State Softball: Schedule 2022, How Much Are The Royals Baseball Worth?, Articles A