load references from crossref.org and opencitations.net. Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0,1)$, and rewards in the range $[-M, M]$, we show how to … Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, and Yinyu Ye. Add a list of citing articles from and to record detail pages. Solving Linear Programs with Sqrt(rank) Linear System Solves. Donate to arXiv. Are there inherent trade-offs between the available memory and the data requirement? Solving Tall Dense Linear Programs in Nearly Linear Time with Yin Tat Lee, Aaron Sidford and Zhao Song. Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Bio Sham Kakade is a professor in the Department of Computer Science and the Department of Statistics at the University of Washington.He works on the mathematical foundations of machine learning and AI. Single Pass Spectral Sparsification in Dynamic Streams by ichael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, and Aaron Sidford. Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time. Faster energy maximization for faster maximum flow. Ramya Vinayak, Weihao Kong, Gregory Valiant, and Sham Kakade, Maximum Likelihood Estimation for Learning Populations … Add a list of references from , , and to record detail pages. Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant: Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. Constantine Caramanis Professor of Electrical and Computer Engineering, UT Austin Verified email at utexas.edu. First theoretic improvement on the running time of linear programming since 1986. Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar. What is the meaning of the colors in the publication lists? Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). with Roy Frostig, Sham M. Kakade and Aaron Sidford. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar. Ambuj Tewari | अम्बुज तिवारी Associate Professor, Department of Statistics and Department of EECS, University of Michigan Verified email at umich.edu. What is the meaning of the colors in the coauthor index? Jonathan A. Kelner Lorenzo Orecchia Yin Tat Lee Aaron Sidford. How does dblp detect coauthor communities. Commun. Sham's thesis helped in laying the statistical foundations of reinforcement learning. Large-Scale Methods for Distributionally Robust Optimization. With Aaron Sidford, Mengdi Wang, Yinyu Ye PDF Learning to Control in Metric Space with Optimal Regret ( 57th Annual Allerton Conference on Communication, Control, and Computing , 2019) “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Efficiently Solving MDPs with Stochastic Mirror Descent. Konstantin Makarychev, Yury Makarychev, Madhur Tulsiani, Gautam Kamath, Julia Chuzhoy: Proccedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, STOC 2020, Chicago, IL, USA, June 22-26, 2020. Aaron Sidford, Mengdi Wang, Lin F. Yang, Yinyu Ye: Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. High-precision Estimation of Random Walks in Small Space. Research Interests Applications and Foundations of Machine Learning, Deep Learning and Optimization. Miscellaneous Papers. FOCS 2017: 801-812, 2017 Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. Which authors of this paper are endorsers? Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu: Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs. A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. Computer Science > Data Structures and Algorithms. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Vatsal Sharan, Aaron Sidford, Gregory Valiant STOC, 2019 abstract | pdf | arXiv. Browse v0.3.2.5 released 2020-07-27 Feedback? Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). We would like to express our heartfelt thanks to the many users who have sent us their remarks and constructive critizisms via our survey during the past weeks. Memory-Sample Tradeoffs for Linear Regression with Small Error. Aaron Sidford Stanford University Verified email at stanford.edu. Vol. Semi-Streaming Bipartite Matching in Fewer Passes and Less Space. Bookmark (what is this?) For more information see our F.A.Q. Solving tall dense linear programs in nearly linear time. CoRR abs/1906.11985 (2019) Virtual : IEEE, 2020. Deterministic Approximation of Random Walks in Small Space. Web Presence:Google Scholar;Github;dblp. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Nima Anari's Academic Homepage. Selected Papers: alphabetical ordering of authors (as in CS Theory papers). Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, LIN F. YANG. Full version on arXiv (with Cameron Musco, Praneeth Netrapalli, Aaron Sidford, and Shashanka Ubaru) ITCS, Matrix Completion and Related Problems via Strong Duality Full version on arXiv (with Nina Balcan, Yingyu Liang, and Hongyang Zhang) ... DBLP . Virtual : IEEE, 2020. Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. Title: An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. STOC 2021. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). Anindya De, Michael Saks, Sijian Tang; The number of solutions for random regular NAE-SAT Allan Sly, Nike Sun, Yumeng Zhang; How to determine if a random graph with a fixed degree sequence has a giant component Felix Joos, Guillem … Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. Vol. Bibliographic content of COLT 2019. Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. A Simple Deterministic Algorithm for Edge Connectivity Thatchaphol Saranurak Summary: Computing min cuts in a few pages. Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. CoRR abs/1611.00755 (2016) Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. Path Finding Methods for Linear Programming: Solving Linear Programs in Õ(vrank) Iterations and Faster Algorithms for Maximum Flow. Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. Accelerated Methods for Non-Convex Optimization. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. What is the role of memory in continuous optimization and learning? So please proceed with care and consider checking the Twitter privacy policy. Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni: Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. Solving Tall Dense Linear Programs in Nearly Linear Time with Yin Tat Lee, Aaron Sidford and Zhao Song. Authors: Michael Kapralov, Navid Nouri, Aaron Sidford, Jakab Tardos Download PDF Abstract: In this paper we consider the problem of computing spectral approximations to graphs in the single pass dynamic streaming model. Web Presence:Google Scholar;Github;dblp. 27. Ryan Rogers, Aaron Roth, Adam Smith, Om Thakkar; Computational Efficiency Requires Simple Taxation Shahar Dobzinski; Noisy population recovery in polynomial time. Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. In ICML 2016. AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford: Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. In pursuit of that commitment, SIAM is dedicated to the philosophy of equality of opportunity and treatment for all participants regardless of gender, gender identity or expression, sexual orientation… Kai Sheng Tai, Peter Bailis, and Gregory Valiant, Equivariant Transformer Networks. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). What is the meaning of the colors in the publication lists? A General Framework for Symmetric Property Estimation. Accelerating Stochastic Gradient Descent. Complexity of Highly Parallel Non-Smooth Convex Optimization. Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. Is it possible to achieve the sample complexity of second-order optimization methods with significantly less memory? Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, So please proceed with care and consider checking the Unpaywall privacy policy. with Aaron Bernstein, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford and He Sun. Suvrit Sra MIT Verified email at mit.edu. Deterministic Algorithms for … All settings here will be stored as cookies with your web browser. CoRR abs/1810.02348 (2018) In ICML 2015. This paper considers the problem of canonical-correlation analysis (CCA) (Hotelling, 1936) and, more broadly, the generalized eigenvector problem for a pair of symmetric matrices. Chi Jin, Sham M. Kakade, Cameron Musco, Praneeth Netrapalli, Aaron Sidford: Robust Shift-and-Invert Preconditioning: Faster and More Sample Efficient Algorithms for Eigenvector Computation. Authors: Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford. Add open access links from to the list of external document links (if available). Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 퓁 1-Regression in Nearly Linear Time for Dense Instances. with Aaron Bernstein, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford and He Sun. Improved Girth Approximation and Roundtrip Spanners. Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. Stability of the Lanczos Method for Matrix Function Approximation. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. listing | bibtex. Our algorithms improve upon the fastest running time for empirical risk minimization (ERM), and in particular linear least-squares regression, across a wide range of problem settings. Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. Cambridge, Massachusetts (MIT) : Leibniz International Proceedings in Informatics (LIPIcs), 2019. Yin Tat Lee Aaron Sidford : Nondeterministic Direct Product Reductions and the Success Probability of SAT Solvers. About Me. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. SOSA 2021. Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. Dynamic Streaming Spectral Sparsification in Nearly Linear Time and Space. “Derandomization beyond connectivity: Undirected Laplacian systems in nearly logarithmic space.” 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS `17), 2017. Dynamic Approximate Shortest … 其它链接 ： dblp.uni-trier.de | academic.microsoft.com. All settings here will be stored as cookies with your web browser. So please proceed with care and consider checking the Twitter privacy policy. Full version on arXiv (with Cameron Musco, Praneeth Netrapalli, Aaron Sidford, and Shashanka Ubaru) ITCS, Matrix Completion and Related Problems via Strong Duality Full version on arXiv (with Nina Balcan, Yingyu Liang, and Hongyang Zhang) 2017. My research focuses on developing and applying fast algorithms for machine learning and data science. 26. Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Summary: Generalizing the technique from the previous paper to work with LPs with box constraints. Arboral satisfaction: Recognition and LP approximation. | Disable MathJax (What is MathJax?) To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Yair Carmon יאיר כרמון. Russell Impagliazzo Ramamohan Paturi Stefan Schneider Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. Donate to arXiv. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. CoRR abs/1810.02348 (2018) Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, and Di Wang. Accelerated Methods for NonConvex Optimization. ICML 2019: 5690-5700 Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. 2015 and Earlier 5. Competing with the Empirical Risk Minimizer in a Single Pass. Yair Carmon, John C. Duchi, Oliver Hinder, Aaron Sidford: "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. 关键词 ： open problem planning horizon upper bound sample complexity low bound 更多 (7+) 微博一下 ： There does not exist a lower bound that depends polynomially on the planning horizon. Faster Energy Maximization for Faster Maximum Flow. Near-optimal time and sample complexities for solving markov decision processes with a generative model. the dblp computer science bibliography is funded by: Lower bounds for finding stationary points II: first-order methods. CoRR abs/1710.09430 (2017) Principal Component Projection Without Principal Component Analysis. AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford: Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. ACM 60(4): 86-93 (2017) Jack Murtagh, Omer Reingold, Aaron Sidford, Salil P. Vadhan, Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. Vol. Faster Eigenvector Computation via Shift-and-Invert Preconditioning. Naman Agarwal , Sham Kakade , Rahul Kidambi , Yin Tat Lee , Praneeth Netrapalli , Aaron Sidford , “Leverage Score 145. NIPS, Approximation Algorithms for $\ell_0$-Low Rank Approximation Full version on arXiv with Karl Bringmann and Pavel Kolev ; NIPS, Near Optimal … I am a PhD student in the MIT Theory Group where I am very fortunate to be advised by Erik D. Demaine and Julian Shun.From June 2020 to December 2020, I was a Google Student Researcher with the IOR team in the Google Discrete Algorithms … Best paper and best student paper in FOCS 2014. Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. My name is Lin Yang (杨林). Matching Matrix Bernstein with Little Memory: Near-Optimal Finite Sample Guarantees for Oja's Algorithm. Lower bounds for finding stationary points I. This result is an exponential improvement on the dependency on H over existing upper bounds.2. CoRR abs/1611.00755 (2016) NIPS, Approximation Algorithms for $\ell_0$-Low Rank Approximation Full version on arXiv (with Karl Bringmann and Pavel Kolev) NIPS, Near Optimal … Efficient profile maximum likelihood for universal symmetric property estimation. "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. Manuscript, 2020. Polylogarithmic Fully Retroactive Priority Queues via Hierarchical Checkpointing. Selected Papers: alphabetical ordering of authors (as in CS Theory papers). Acceleration with a Ball Optimization Oracle.

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