Student Intranet. aaron sidford cv natural fibrin removal - libiot.kku.ac.th Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Our method improves upon the convergence rate of previous state-of-the-art linear programming . COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Personal Website. 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. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. >> In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Many of my results use fast matrix multiplication In International Conference on Machine Learning (ICML 2016). [pdf] ?_l) 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 . My CV. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. Conference on Learning Theory (COLT), 2015. the Operations Research group. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Aaron Sidford | Management Science and Engineering ReSQueing Parallel and Private Stochastic Convex Optimization. Iterative methods, combinatorial optimization, and linear programming with Yair Carmon, Arun Jambulapati and Aaron Sidford ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. [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. missouri noodling association president cnn. ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. AISTATS, 2021. Here are some lecture notes that I have written over the years. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! MS&E213 / CS 269O - Introduction to Optimization Theory My research focuses on AI and machine learning, with an emphasis on robotics applications. View Full Stanford Profile. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games 5 0 obj "t a","H 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. Articles Cited by Public access. Some I am still actively improving and all of them I am happy to continue polishing. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. 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. to be advised by Prof. Dongdong Ge. I graduated with a PhD from Princeton University in 2018. ICML, 2016. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs I am an Assistant Professor in the School of Computer Science at Georgia Tech. with Yair Carmon, Aaron Sidford and Kevin Tian With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat The design of algorithms is traditionally a discrete endeavor. Source: appliancesonline.com.au. by 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. 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 . [pdf] Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games when do tulips bloom in maryland; indo pacific region upsc The authors of most papers are ordered alphabetically. /CreationDate (D:20230304061109-08'00') Roy Frostig, Sida Wang, Percy Liang, Chris Manning. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). of practical importance. Yair Carmon. SHUFE, where I was fortunate Applying this technique, we prove that any deterministic SFM algorithm . She was 19 years old and looking - freewareppc.com UGTCS 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 Yair Carmon, Aaron Sidford and Kevin Tian [pdf] [talk] To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. 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. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Faster Matroid Intersection Princeton University IEEE, 147-156. << Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. A Faster Algorithm for Linear Programming and the Maximum Flow Problem II Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Semantic parsing on Freebase from question-answer pairs. Interior Point Methods for Nearly Linear Time Algorithms | ISL I am broadly interested in optimization problems, sometimes in the intersection with machine learning Aaron Sidford - Stanford University With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Aaron Sidford. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Full CV is available here. Aaron's research interests lie in optimization, the theory of computation, and the . 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. Group Resources. Publications | Salil Vadhan Jan van den Brand 4 0 obj My interests are in the intersection of algorithms, statistics, optimization, and machine learning. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Efficient Convex Optimization Requires Superlinear Memory. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Two months later, he was found lying in a creek, dead from . Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). with Aaron Sidford en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. In each setting we provide faster exact and approximate algorithms. % CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. University, where COLT, 2022. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . dblp: Daogao Liu Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. publications by categories in reversed chronological order. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). 2016. If you see any typos or issues, feel free to email me. . xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Anup B. Rao - Google Scholar I am a senior researcher in the Algorithms group at Microsoft Research Redmond. F+s9H [pdf] [poster] small tool to obtain upper bounds of such algebraic algorithms. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Mary Wootters - Google [pdf] [talk] [poster] I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. The following articles are merged in Scholar. [PDF] Faster Algorithms for Computing the Stationary Distribution Yujia Jin. One research focus are dynamic algorithms (i.e. 2021 - 2022 Postdoc, Simons Institute & UC . . Stanford University . I regularly advise Stanford students from a variety of departments. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford The system can't perform the operation now. Email: sidford@stanford.edu. Aaron Sidford Stanford University Verified email at stanford.edu. /Length 11 0 R I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). CME 305/MS&E 316: Discrete Mathematics and Algorithms Lower bounds for finding stationary points II: first-order methods. Their, This "Cited by" count includes citations to the following articles in Scholar. Publications and Preprints. Yang P. Liu, Aaron Sidford, Department of Mathematics Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. O! Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. My long term goal is to bring robots into human-centered domains such as homes and hospitals. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. resume/cv; publications. Follow. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. [pdf] [slides] ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Aaron Sidford - My Group with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian She was 19 years old and looking forward to the start of classes and reuniting with her college pals. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration This site uses cookies from Google to deliver its services and to analyze traffic. Email / 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. 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. Sequential Matrix Completion. University of Cambridge MPhil. It was released on november 10, 2017. >> ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in pdf, Sequential Matrix Completion. %PDF-1.4 CV (last updated 01-2022): PDF Contact. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. In this talk, I will present a new algorithm for solving linear programs. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. dblp: Yin Tat Lee Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Lower Bounds for Finding Stationary Points II: First-Order Methods Simple MAP inference via low-rank relaxations. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Yujia Jin - Stanford University Neural Information Processing Systems (NeurIPS), 2014. Accelerated Methods for NonConvex Optimization | Semantic Scholar ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. 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. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. with Arun Jambulapati, Aaron Sidford and Kevin Tian Allen Liu - GitHub Pages Aaron Sidford's Homepage - Stanford University [pdf] [poster] 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. Faster energy maximization for faster maximum flow. We also provide two . There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Aaron Sidford - Google Scholar Google Scholar; Probability on trees and . Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. With Yair Carmon, John C. Duchi, and Oliver Hinder. Kirankumar Shiragur | Data Science We forward in this generation, Triumphantly. 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. Aaron Sidford - Selected Publications Contact. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). aaron sidford cvnatural fibrin removalnatural fibrin removal About Me. My research is on the design and theoretical analysis of efficient algorithms and data structures. Aaron Sidford's research works | Stanford University, CA (SU) and other [pdf] Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Anup B. Rao. 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).