Andre Wibisono

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Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyZweryfikowany adres z cs.berkeley.edu
Ashia C. WilsonAssistant Professor at MITZweryfikowany adres z mit.edu
Jacob AbernethyAssistant Professor, University of MichiganZweryfikowany adres z umich.edu
John DuchiAssociate Professor of Statistics and Electrical Engineering, Stanford UniversityZweryfikowany adres z stanford.edu
Santosh S. VempalaGeorgia TechZweryfikowany adres z gatech.edu
Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyZweryfikowany adres z mit.edu
Nicholas BoydUC BerkeleyZweryfikowany adres z berkeley.edu
Lorenzo RosascoMaLGa Machine Learning Genoa Center - Università degli Studi di GenovaZweryfikowany adres z unige.it
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITZweryfikowany adres z ai.mit.edu
Jun-Kun WangUniversity of California San DiegoZweryfikowany adres z ucsd.edu
Rafael FrongilloComputer Science, CU BoulderZweryfikowany adres z colorado.edu
Varun JogUniversity of CambridgeZweryfikowany adres z cam.ac.uk
Yongxin ChenAssociate Professor, Georgia Institute of TechnologyZweryfikowany adres z gatech.edu
Molei TaoAssociate Professor, Georgia Institute of TechnologyZweryfikowany adres z gatech.edu
Lester MackeyMicrosoft ResearchZweryfikowany adres z microsoft.com
Kaylee Y. YangPhD student in Statistics and Data Science, Yale UniversityZweryfikowany adres z yale.edu
Po-Ling LohUniversity of CambridgeZweryfikowany adres z cam.ac.uk
Sinho ChewiInstitute for Advanced StudyZweryfikowany adres z ias.edu
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyZweryfikowany adres z cs.berkeley.edu
Bin HuUniversity of Illinois at Urbana-ChampaignZweryfikowany adres z illinois.edu

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Andre Wibisono

Assistant Professor at Yale University

Zweryfikowany adres z yale.edu - Strona główna

Machine learning optimization sampling game theory


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
A variational perspective on accelerated methods in optimization A Wibisono, AC Wilson, MI Jordan proceedings of the National Academy of Sciences 113 (47), E7351-E7358, 2016	489	2016
Optimal rates for zero-order convex optimization: The power of two function evaluations JC Duchi, MI Jordan, MJ Wainwright, A Wibisono IEEE Transactions on Information Theory 61 (5), 2788-2806, 2015	436	2015
Streaming variational bayes T Broderick, N Boyd, A Wibisono, AC Wilson, MI Jordan Advances in neural information processing systems 26, 2013	371	2013
Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices S Vempala, A Wibisono Advances in neural information processing systems 32, 2019	178	2019
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem A Wibisono Conference on Learning Theory, 2093-3027, 2018	137	2018
Last-iterate convergence rates for min-max optimization J Abernethy, KA Lai, A Wibisono arXiv preprint arXiv:1906.02027, 2019	62	2019
Maximum entropy distributions on graphs C Hillar, A Wibisono arXiv preprint arXiv:1301.3321, 2013	58	2013
Accelerating rescaled gradient descent: Fast optimization of smooth functions AC Wilson, L Mackey, A Wibisono Advances in Neural Information Processing Systems 32, 2019	46	2019
Proximal langevin algorithm: Rapid convergence under isoperimetry A Wibisono arXiv preprint arXiv:1911.01469, 2019	35	2019
Improved analysis for a proximal algorithm for sampling Y Chen, S Chewi, A Salim, A Wibisono Conference on Learning Theory, 2984-3014, 2022	31	2022
Last-iterate convergence rates for min-max optimization: Convergence of hamiltonian gradient descent and consensus optimization J Abernethy, KA Lai, A Wibisono Algorithmic Learning Theory, 3-47, 2021	31	2021
Finite sample convergence rates of zero-order stochastic optimization methods A Wibisono, MJ Wainwright, M Jordan, JC Duchi Advances in Neural Information Processing Systems 25, 2012	31	2012
On accelerated methods in optimization A Wibisono, AC Wilson arXiv preprint arXiv:1509.03616, 2015	29	2015
Minimax option pricing meets Black-Scholes in the limit J Abernethy, RM Frongillo, A Wibisono Proceedings of the forty-fourth annual ACM symposium on Theory of computing …, 2012	26	2012
Sufficient conditions for uniform stability of regularization algorithms A Wibisono, L Rosasco, T Poggio Computer Science and Artificial Intelligence Laboratory Technical Report …, 2009	16	2009
The mirror Langevin algorithm converges with vanishing bias R Li, M Tao, SS Vempala, A Wibisono International Conference on Algorithmic Learning Theory, 718-742, 2022	15	2022
How to hedge an option against an adversary: Black-scholes pricing is minimax optimal J Abernethy, PL Bartlett, R Frongillo, A Wibisono Advances in neural information processing systems 26, 2013	15	2013
Provable acceleration of heavy ball beyond quadratics for a class of polyak-lojasiewicz functions when the non-convexity is averaged-out JK Wang, CH Lin, A Wibisono, B Hu International Conference on Machine Learning, 22839-22864, 2022	13	2022
Inverses of symmetric, diagonally dominant positive matrices and applications CJ Hillar, S Lin, A Wibisono arXiv preprint arXiv:1203.6812, 2012	13	2012
Convergence of the Inexact Langevin Algorithm and Score-based Generative Models in KL Divergence K Yingxi Yang, A Wibisono arXiv e-prints, arXiv: 2211.01512, 2022	12*	2022

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