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Andre Wibisono
Andre Wibisono
Assistant Professor at Yale University
yale.edu의 이메일 확인됨 - 홈페이지
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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
3992016
Streaming variational bayes
T Broderick, N Boyd, A Wibisono, AC Wilson, MI Jordan
Advances in neural information processing systems 26, 2013
3382013
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
3172015
Rapid convergence of the unadjusted langevin algorithm: Isoperimetry suffices
S Vempala, A Wibisono
Advances in neural information processing systems 32, 2019
932019
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
822018
Last-iterate convergence rates for min-max optimization
J Abernethy, KA Lai, A Wibisono
arXiv preprint arXiv:1906.02027, 2019
522019
Maximum entropy distributions on graphs
C Hillar, A Wibisono
arXiv preprint arXiv:1301.3321, 2013
502013
Accelerating rescaled gradient descent: Fast optimization of smooth functions
AC Wilson, L Mackey, A Wibisono
Advances in Neural Information Processing Systems 32, 2019
352019
On accelerated methods in optimization
A Wibisono, AC Wilson
arXiv preprint arXiv:1509.03616, 2015
252015
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
242012
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
232012
Proximal Langevin algorithm: Rapid convergence under isoperimetry
A Wibisono
arXiv preprint arXiv:1911.01469, 2019
182019
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
152021
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
122013
Sufficient conditions for uniform stability of regularization algorithms
A Wibisono, L Rosasco, T Poggio
Computer Science and Artificial Intelligence Laboratory Technical Report …, 2009
122009
Generalization and properties of the neural response
J Bouvrie, T Poggio, L Rosasco, S Smale, A Wibisono
82010
Learning and invariance in a family of hierarchical kernels
A Wibisono, J Bouvrie, L Rosasco, T Poggio
82010
Information and estimation in Fokker-Planck channels
A Wibisono, V Jog, PL Loh
2017 IEEE International Symposium on Information Theory (ISIT), 2673-2677, 2017
72017
Optimal rates for zero-order optimization: the power of two function evaluations
JC Duchi, MI Jordan, MJ Wainwright, A Wibisono
arXiv preprint arXiv:1312.2139, 2013
72013
Inverses of symmetric, diagonally dominant positive matrices and applications
CJ Hillar, S Lin, A Wibisono
arXiv preprint arXiv:1203.6812, 2012
72012
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