aaron sidford dblp
Is it possible to achieve the sample complexity of second-order optimization methods with significantly less memory? Donate to arXiv. A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. Memory-sample tradeoffs for linear regression with small error. Invited to the special issue Browse v0.3.2.5 released 2020-07-27 Feedback? Recent Papers on Streaming Algorithms for Clustering . Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. Virtual : IEEE, 2020. This result is an exponential improvement on the dependency on H over existing upper bounds.2. ICML 2017 : 654-663 FOCS 2016 - List of Accepted Papers; Robust Estimators in High Dimensions without the Computational Intractability Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. load references from crossref.org and opencitations.net. Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. 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. with Aaron Bernstein, Maximilian Probst Gutenberg, Danupon Nanongkai, Thatchaphol Saranurak, Aaron Sidford and He Sun. Efficient Structured Matrix Recovery and Nearly-Linear Time Algorithms for Solving Inverse Symmetric M-Matrices. Stability of the Lanczos Method for Matrix Function Approximation. Solving Tall Dense Linear Programs in Nearly Linear Time. AmirMahdi Ahmadinejad, Arun Jambulapati, Amin Saberi, Aaron Sidford: Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. with Roy Frostig, Sham M. Kakade and Aaron Sidford. DBLP - CS Bibliography. Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth: Guilt-free data reuse. Fast and Space Efficient Spectral Sparsification in Dynamic Streams. STOC 2021. 145. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. Near-optimal method for highly smooth convex optimization. Manuscript, 2020. CoRR abs/1510.08896 (2015) Polylogarithmic Fully Retroactive Priority Queues via Hierarchical Checkpointing. Privacy notice: By enabling the option above, your browser will contact twitter.com and twimg.com to load tweets curated by our Twitter account. Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). Robert M. Freund, Paul Grigas, New analysis and results for the Frank–Wolfe method Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). Acceleration with a Ball Optimization Oracle. 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. Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant: Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. Which authors of this paper are endorsers? Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. Faster energy maximization for faster maximum flow. A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Download PDF Abstract: We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Authors: Jonathan A. Kelner, Lorenzo Orecchia, Yin Tat Lee, Aaron Sidford (Submitted … CoRR abs/1611.00755 (2016) Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Aaron Sidford Stanford University Verified email at stanford.edu. Bibliographic content of COLT 2019. So please proceed with care and consider checking the Twitter privacy policy. Accelerated Methods for Non-Convex Optimization. Vol. Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation. SOSA 2021. SA-TTA 2021 The 9th track on Software Architecture: Theory, Technology, and Applications (SA-TTA) at the 36th ACM/SIGAPP Symposium On Applied Computing (SAC 2021) : CSEIT 2021 8th International Conference on Computer Science, Engineering and Information Technology : CFMAI 2021 2021 3rd International Conference on Frontiers of Mathematics and Artificial Intelligence (CFMAI 2021) 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 … Dynamic Approximate Shortest … To protect your privacy, all features that rely on external API calls from your browser are turned off by default. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. 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. | Disable MathJax (What is MathJax?) I obtained my Ph.D. in Computer Science at UC Berkeley.At Berkeley, I was advised by Satish Rao, and was part of the Theory … Faster Divergence Maximization for Faster Maximum Flow. We raise these questions, and show the first … What is the role of memory in continuous optimization and learning? 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. Arboral satisfaction: Recognition and LP approximation. the dblp computer science bibliography is funded by: Lower bounds for finding stationary points II: first-order methods. Web Presence:Google Scholar;Github;dblp. Faster Energy Maximization for Faster Maximum Flow. Computer Science > Data Structures and Algorithms. A decomposition of a graph G=(V,E) is a partition of the vertex set into subsets (called blocks). Authors: Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye. So please proceed with care and consider checking the Twitter privacy policy. Is it possible to achieve the sample complexity of second-order optimization methods with significantly less memory? Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. CoRR abs/2101.05719 (2021) Semi-Streaming Bipartite Matching in Fewer Passes and Less Space. Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. with Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford. Accelerated Methods for NonConvex Optimization. My research focuses on developing and applying fast algorithms for machine learning and data science. Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. For more information see our F.A.Q. A paper qualifies as a student paper if all authors are full-time students at the date of the submission. Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. 100% of your contribution will fund improvements and new initiatives to benefit arXiv's global scientific community. Add a list of references from , , and to record detail pages. load references from crossref.org and opencitations.net. CoRR abs/1810.02348 (2018) Ramya Vinayak, Weihao Kong, Gregory Valiant, and Sham Kakade, Maximum Likelihood Estimation for Learning Populations … Parallel Reachability in Almost Linear Work and Square Root Depth. Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm. How does dblp detect coauthor communities. Single Pass Spectral Sparsification in Dynamic Streams. LIN F. YANG. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. ICML, 2019. Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, and Yinyu Ye. Selected Papers: alphabetical ordering of authors (as in CS Theory papers). We develop a family of accelerated stochastic algorithms that minimize sums of convex functions. Authors: Roy Frostig, Cameron Musco, Christopher Musco, Aaron Sidford. Towards Optimal Running Times for Optimal Transport. 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. Accelerating Stochastic Gradient Descent for Least Squares Regression. Pattern-avoiding Access in Binary Search Trees [] [slides part 1, part 2 (by L.K.)] 26. 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. Jonathan A. Kelner Lorenzo Orecchia Yin Tat Lee Aaron Sidford. 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 . Acceleration with a Ball Optimization Oracle. Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. Research Interests Applications and Foundations of Machine Learning, Deep Learning and Optimization. Virtual : IEEE, 2020. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization. Yair Carmon יאיר כרמון. Vol. 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. A simple, combinatorial algorithm for solving SDD systems in nearly-linear time. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. 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. Miscellaneous Papers. Rong Ge, Chi Jin, Sham M. Kakade, Praneeth Netrapalli, Aaron Sidford, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation 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. About Me. Competing with the Empirical Risk Minimizer in a Single Pass. Solving Tall Dense Linear Programs in Nearly Linear Time with Yin Tat Lee, Aaron Sidford and Zhao Song. In Advances in Neural Information Processing Systems, pages 5186–5196, 2018a. Add a list of citing articles from and to record detail pages. Oliver Hinder, Aaron Sidford, Nimit Sharad Sohoni: Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond. Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems. Selected Papers: alphabetical ordering of authors (as in CS Theory papers). A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. “High-precision estimation of random walks in small space.” 61st Annual IEEE Symposium on the Foundations of Computer Science (FOCS 2020). At the same time, Twitter will persistently store several cookies with your web browser. STOC 2020. I am an assistant professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. 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. Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time. Manuscript, 2020. Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, A Rank-1 Sketch for Matrix Multiplicative Weights. A General Framework for Symmetric Property Estimation. Bibliographic content of COLT 2019. In ICML 2015. First theoretic improvement on the running time of linear programming since 1986. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. About; Help 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) 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 … 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. Yin Tat Lee Aaron Sidford : Nondeterministic Direct Product Reductions and the Success Probability of SAT Solvers. CoRR abs/1710.09430 (2017) Add a list of references from , , and to record detail pages. At the same time, Twitter will persistently store several cookies with your web browser. Subquadratic submodular function minimization. Efficiently Solving MDPs with Stochastic Mirror Descent. Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar. Title: An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations. What is the meaning of the colors in the publication lists? … In ICML 2016. Efficient Õ(n/ε) Spectral Sketches for the Laplacian and its Pseudoinverse. Russell Impagliazzo Ramamohan Paturi Stefan Schneider 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. 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. Correlation clustering in data streams by K. J. Ahn, G. Cormode, S. Guha, A. McGregor, and A. Wirth. Parallel Reachability in Almost Linear Work and Square Root Depth. You need to opt-in for them to become active. Aaron Sidford Mengdi Wang Xian Wu Yinyu Ye. Solving Tall Dense Linear Programs in Nearly Linear Time with Yin Tat Lee, Aaron Sidford and Zhao Song. Memory-Sample Tradeoffs for Linear Regression with Small Error. Commun. 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 … Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). Ahmadinejad, AmirMahdi, Jonathan Kelner, Jack Murtagh, John Peebles, Aaron Sidford, and Salil Vadhan. Ambuj Tewari | अम्बुज तिवारी Associate Professor, Department of Statistics and Department of EECS, University of Michigan Verified email at umich.edu. The … 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. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available). Efficient Convex Optimization with Membership Oracles. Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. the dblp computer science bibliography is funded by: Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers. 2017. Vol. Constantine Caramanis Professor of Electrical and Computer Engineering, UT Austin Verified email at utexas.edu. Bookmark (what is this?) [video@FOCS'15] Parinya Chalermsook, Mayank Goswami, László Kozma, Kurt Mehlhorn and Thatchaphol Saranurak FOCS 2015, HALG 2016 (Contributed talk) Summary: We apply a well-studied forbidden submatrix theory to a geometric view of the execution log of Greedy, a binary search tree … Algorithms and Techniques (APPROX/RANDOM 2019), Dimitris Achlioptas and László A. Végh (Eds.). Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. “Derandomization beyond connectivity: Undirected Laplacian systems in nearly logarithmic space.” 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS `17), 2017. Nima Anari's Academic Homepage. A Simple Deterministic Algorithm for Edge Connectivity Thatchaphol Saranurak Summary: Computing min cuts in a few pages. Solving tall dense linear programs in nearly linear time. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Summary: Generalizing the technique from the previous paper to work with LPs with box constraints. Faster Eigenvector Computation via Shift-and-Invert Preconditioning. CoRR abs/1906.11985 (2019) So please proceed with care and consider checking the Unpaywall privacy policy. Aaron Sidford (MIT) Yin Tat Lee (MIT) "Path-Finding Methods for Linear Programming : Solving Linear Programs in Õ(√rank) Iterations and Faster Algorithms for Maximum Flow" 2013: Jonah Sherman (University of California, Berkeley) "Nearly Maximum Flows in Nearly Linear Time" 2012: Nir Bitansky (Tel Aviv University), Omer Paneth (Boston University) "From the Impossibility of Obfuscation to a New Non … Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford: A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). 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. 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. A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares). Are there inherent trade-offs between the available memory and the data requirement? Ahmadinejad, AmirMahdi, Jonathan Kelner, Jack Murtagh, John Peebles, Aaron Sidford, and Salil Vadhan. Research Interests Applications and Foundations of Machine Learning, Deep Learning and Optimization. A Direct Õ(1/ε) Iteration Parallel Algorithm for Optimal Transport. High-precision Estimation of Random Walks in Small Space. So please proceed with care and consider checking the Internet Archive privacy policy. Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. Deterministic Approximation of Random Walks in Small Space. Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging. By … 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. NIPS, Approximation Algorithms for $\ell_0$-Low Rank Approximation Full version on arXiv with Karl Bringmann and Pavel Kolev ; NIPS, Near Optimal … 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. A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport. 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. Large-Scale Methods for Distributionally Robust Optimization. 2015 and Earlier 5. Murtagh, Jack, Omer Reingold, Aaron Sidford, and Salil Vadhan. Abhradeep Guha Thakurta Assistant Professor, University of California Santa Cruz Verified email at ucsc.edu. What is the role of memory in continuous optimization and learning? Coordinate Methods for Accelerating ℓ∞ Regression and Faster Approximate Maximum Flow. CoRR abs/1908.11071 ( 2019 ) 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. Lower bounds for finding stationary points I. 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. CoRR abs/1810.02348 (2018) 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. 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. Andrew Drucker : A Satisfiability Algorithm for Sparse Depth Two Threshold Circuits. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. What is the meaning of the colors in the publication lists? Subquadratic Submodular Function Minimization. Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration. ICML, 2019. These are two fundamental problems in data analysis and scientific computing with numerous applications in machine learning and statistics (Shi … "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions. 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. Please join the Simons Foundation and our generous member organizations in supporting arXiv during our giving campaign September 23-27. ICML 2019: 5690-5700 STOC 2020. Complexity of Highly Parallel Non-Smooth Convex Optimization. Aaron Sidford, Stanford University, USA Gerth Stølting Brodal, Aarhus University, Denmark Mikkel Thorup, University of Copenhagen, Denmark Virginia Vassilevska Williams, Stanford University, USA Dorothea Wagner, Karlsruhe Institute of Technology, Germany Roger Wattenhofer, ETH Zurich, Switzerland Matt Weinberg, Princeton University, USA Peter Winkler, Dartmouth College, USA Grigory Yaroslavtsev, … Vol. Sham's thesis helped in laying the statistical foundations of reinforcement learning. Kai Sheng Tai, Peter Bailis, and Gregory Valiant, Equivariant Transformer Networks. Constant girth approximation for directed graphs in subquadratic time. Vatsal Sharan, Kai Sheng Tai, Peter Bailis, and Gregory Valiant, Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. “ Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. Efficient Inverse Maintenance and Faster Algorithms for Linear Programming. “Deterministic approximation of random walks in small space.” In Approximation, Randomization, and Combinatorial Optimization. The Machtey Award is awarded at the annual IEEE Symposium on Foundations of Computer Science (FOCS) to the author(s) of the best student paper(s).
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