Bookmark (what is this?) Which authors of this paper are endorsers? Sham's thesis helped in laying the statistical foundations of reinforcement learning. Kai Sheng Tai, Peter Bailis, and Gregory Valiant, Equivariant Transformer Networks. Donate to arXiv. Subquadratic Submodular Function Minimization. About; Help load references from crossref.org and opencitations.net. 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. Ambuj Tewari | अम्बुज तिवारी Associate Professor, Department of Statistics and Department of EECS, University of Michigan Verified email at umich.edu. Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation. Jonathan A. Kelner Lorenzo Orecchia Yin Tat Lee Aaron Sidford. Virtual : IEEE, 2020. load references from crossref.org and opencitations.net. CoRR abs/1611.00755 (2016) First theoretic improvement on the running time of linear programming since 1986. The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood. Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. Near-optimal time and sample complexities for solving markov decision processes with a generative model. Naman Agarwal , Sham Kakade , Rahul Kidambi , Yin Tat Lee , Praneeth Netrapalli , Aaron Sidford , “Leverage Score CoRR abs/1906.11985 (2019) 145. Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. Single Pass Spectral Sparsification in Dynamic Streams. Efficient profile maximum likelihood for universal symmetric property estimation. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. CoRR abs/1810.02348 (2018) Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. Niranjan Uma Naresh (U.N.Niranjan) Microsoft Verified email at uci.edu. Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More. Invited to the special issue An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, What is the meaning of the colors in the publication lists? Naman Agarwal , Sham Kakade , Rahul Kidambi , Yin Tat Lee , Praneeth Netrapalli , Aaron Sidford , “Leverage Score Sampling for Faster Accelerated Regression and ERM”, In Conference on … Is it possible to achieve the sample complexity of second-order optimization methods with significantly less memory? 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. Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. Memory-sample tradeoffs for linear regression with small error. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Yair Carmon יאיר כרמון. By … Leverage Score Sampling for Faster Accelerated Regression and ERM. 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. Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. Principal Component Projection Without Principal Component Analysis. Suvrit Sra MIT Verified email at mit.edu. Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. Add a list of references from , , and to record detail pages. Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs. Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni: Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. last updated on 2021-03-12 00:49 CET by the dblp team, all metadata released as open data under CC0 1.0 license, see also: Terms of Use | Privacy Policy | Imprint. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Uniform Sampling for Matrix Approximation. Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. SOSA 2021. Acceleration with a Ball Optimization Oracle. Matching the Universal Barrier Without Paying the Costs : Solving Linear Programs with Õ(sqrt(rank)) Linear System Solves. Improved Girth Approximation and Roundtrip Spanners. I am an assistant professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. 2015 and Earlier 5. ICML 2017 : 654-663 CoRR abs/1510.08896 (2015) We raise these questions, and show the first … Donate to arXiv. Summary: Generalizing the technique from the previous paper to work with LPs with box constraints. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. 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) 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. Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. Aaron Sidford Stanford University Verified email at stanford.edu. Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. Best paper and best student paper in FOCS 2014. Privacy notice: By enabling the option above, your browser will contact twitter.com and twimg.com to load tweets curated by our Twitter account. 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. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). with Aaron Bernstein, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford and He Sun. “High-precision estimation of random walks in small space.” 61st Annual IEEE Symposium on the Foundations of Computer Science (FOCS 2020). Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. Complexity of Highly Parallel Non-Smooth Convex Optimization. Solving Tall Dense Linear Programs in Nearly Linear Time. Add open access links from to the list of external document links (if available). 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. We develop a family of accelerated stochastic algorithms that minimize sums of convex functions. Russell Impagliazzo Ramamohan Paturi Stefan Schneider Deterministic Algorithms for … Correlation clustering in data streams by K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth. | Disable MathJax (What is MathJax?) Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. ICML, 2019. with Aaron Bernstein, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford and He Sun. ICML, 2019. Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. Add open access links from to the list of external document links (if available). Faster Eigenvector Computation via Shift-and-Invert Preconditioning. Accelerating Stochastic Gradient Descent for Least Squares Regression. the dblp computer science bibliography is funded by: Lower bounds for finding stationary points II: first-order methods. Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Constantine Caramanis Professor of Electrical and Computer Engineering, UT Austin Verified email at utexas.edu. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. Following the Path of Least Resistance : An Õ(m sqrt(n)) Algorithm for the Minimum Cost Flow Problem. These are two fundamental problems in data analysis and scientific computing with numerous applications in machine learning and statistics (Shi … Subquadratic submodular function minimization. Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). A General Framework for Symmetric Property Estimation. Single Pass Spectral Sparsification in Dynamic Streams by ichael Kapralov, Yin Tat Lee, Cameron Musco, Christopher Musco, and Aaron Sidford. For more information see our F.A.Q. with Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford. Yin Tat Lee Aaron Sidford : Nondeterministic Direct Product Reductions and the Success Probability of SAT Solvers. While we did signal Twitter to not track our users by setting the "dnt" flag, we do not have any control over how Twitter uses your data. NIPS, Approximation Algorithms for $\ell_0$-Low Rank Approximation Full version on arXiv with Karl Bringmann and Pavel Kolev ; NIPS, Near Optimal … Full version on arXiv with Cameron Musco, Praneeth Netrapalli, My research focuses on developing and applying fast algorithms for machine learning and data science. Is it possible to achieve the sample complexity of second-order optimization methods with significantly less memory? Ryan Rogers, Aaron Roth, Adam Smith, Om Thakkar; Computational Efficiency Requires Simple Taxation Shahar Dobzinski; Noisy population recovery in polynomial time. At the same time, Twitter will persistently store several cookies with your web browser. Ramya Vinayak, Weihao Kong, Gregory Valiant, and Sham Kakade, Maximum Likelihood Estimation for Learning Populations … To protect your privacy, all features that rely on external API calls from your browser are turned off by default. Competing with the Empirical Risk Minimizer in a Single Pass. Faster Energy Maximization for Faster Maximum Flow. You need to opt-in for them to become active. Acceleration with a Ball Optimization Oracle. 145. 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. So please proceed with care and consider checking the Unpaywall privacy policy. Are there inherent trade-offs between the available memory and the data requirement? Positive semidefinite programming: mixed, parallel, and width-independent. All settings here will be stored as cookies with your web browser.