Allen Liu. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Assistant Professor of Management Science and Engineering and of Computer Science. 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. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs It was released on november 10, 2017. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). 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 maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan 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. Student Intranet. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). However, even restarting can be a hard task here. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration in Mathematics and B.A. of practical importance. I was fortunate to work with Prof. Zhongzhi Zhang. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. I completed my PhD at small tool to obtain upper bounds of such algebraic algorithms. 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 . Aaron Sidford ([email protected]) 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. Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). I am a senior researcher in the Algorithms group at Microsoft Research Redmond. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 475 Via Ortega "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. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. % Lower bounds for finding stationary points II: first-order methods. Email: [email protected]. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. by Aaron Sidford. /CreationDate (D:20230304061109-08'00') I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. [pdf] In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . 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. 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. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Faculty Spotlight: Aaron Sidford. [pdf] with Yair Carmon, Aaron Sidford and Kevin Tian Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization Articles Cited by Public access. This site uses cookies from Google to deliver its services and to analyze traffic. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper publications by categories in reversed chronological order. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 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 In submission. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries 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. " 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. he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. Conference Publications 2023 The Complexity of Infinite-Horizon General-Sum Stochastic Games With Yujia Jin, Vidya Muthukumar, Aaron Sidford To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv) 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration With Yair Carmon, With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. I am fortunate to be advised by Aaron Sidford . ?_l) 4 0 obj Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . 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. 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 2021. Email: [name]@stanford.edu I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. 113 * 2016: The system can't perform the operation now. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 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 Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. I received a B.S. aaron sidford cvnatural fibrin removalnatural fibrin removal Source: appliancesonline.com.au. endobj . SODA 2023: 5068-5089. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. Alcatel flip phones are also ready to purchase with consumer cellular. In International Conference on Machine Learning (ICML 2016). 2021 - 2022 Postdoc, Simons Institute & UC . The system can't perform the operation now. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Information about your use of this site is shared with Google. . ", Applied Math at Fudan I regularly advise Stanford students from a variety of departments. Stanford University with Aaron Sidford Source: www.ebay.ie AISTATS, 2021. ", "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 is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. [pdf] From 2016 to 2018, I also worked in With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). I am an Assistant Professor in the School of Computer Science at Georgia Tech. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. . Aaron Sidford Stanford University Verified email at stanford.edu. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. Research Institute for Interdisciplinary Sciences (RIIS) at The design of algorithms is traditionally a discrete endeavor. Google Scholar; Probability on trees and . pdf, Sequential Matrix Completion. [pdf] [poster] Yin Tat Lee and Aaron Sidford. SHUFE, where I was fortunate Roy Frostig, Sida Wang, Percy Liang, Chris Manning. STOC 2023. Summer 2022: I am currently a research scientist intern at DeepMind in London. with Yair Carmon, Aaron Sidford and Kevin Tian Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Selected recent papers . Management Science & Engineering Aaron Sidford. ReSQueing Parallel and Private Stochastic Convex Optimization. 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 Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. << Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games [pdf] theory and graph applications. 2017. My research is on the design and theoretical analysis of efficient algorithms and data structures. IEEE, 147-156. ", "Team-convex-optimization for solving discounted and average-reward MDPs! the Operations Research group. In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Articles 1-20. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG CV (last updated 01-2022): PDF Contact. Thesis, 2016. pdf. Yang P. Liu, Aaron Sidford, Department of Mathematics Yujia Jin. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. If you see any typos or issues, feel free to email me. Publications and Preprints. with Yair Carmon, Kevin Tian and Aaron Sidford ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). COLT, 2022. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. By using this site, you agree to its use of cookies. Improved Lower Bounds for Submodular Function Minimization. Follow. in Chemistry at the University of Chicago. Improves the stochas-tic convex optimization problem in parallel and DP setting. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford 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. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. in math and computer science from Swarthmore College in 2008. Yujia Jin. Abstract. %PDF-1.4 Another research focus are optimization algorithms. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! 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. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. 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. Np%p `a!2D4! O! I am broadly interested in mathematics and theoretical computer science. I am fortunate to be advised by Aaron Sidford. With Cameron Musco and Christopher Musco. One research focus are dynamic algorithms (i.e. /Length 11 0 R If you see any typos or issues, feel free to email me. Huang Engineering Center They will share a $10,000 prize, with financial sponsorship provided by Google Inc. 2016. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching 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. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs [pdf] Here is a slightly more formal third-person biography, and here is a recent-ish CV. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Computer Science. Their, This "Cited by" count includes citations to the following articles in Scholar. Annie Marsden. 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. ! Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. 2013. 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. Many of my results use fast matrix multiplication with Aaron Sidford 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University I am Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. [last name]@stanford.edu where [last name]=sidford. Algorithms Optimization and Numerical Analysis. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. University of Cambridge MPhil. Secured intranet portal for faculty, staff and students. . (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. The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation.
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