image:wibisono
      Andre Wibisono

YALE UNIVERSITY

andre.wibisono [at] yale [dot] edu

I am an assistant professor in the Department of Computer Science at Yale University, with a secondary appointment in the Department of Statistics & Data Science. My research interests are in the design and analysis of algorithms for machine learning, in particular for problems in optimization, sampling, and game dynamics.

I received my BS degrees in Mathematics and in Computer Science from MIT. I received my MEng in Computer Science from MIT, advised by Tomaso Poggio. I received by MA in Statistics from UC Berkeley, and my PhD in Computer Science from UC Berkeley, advised by Michael I. Jordan. Before joining Yale in 2021, I have done postdoctoral research at UW Madison and Georgia Tech.


TEACHING

CPSC 486/586: Probabilistic Machine Learning (Spring 2024, Spring 2023)
CPSC/ECON 365: Algorithms (Fall 2023, Spring 2022)
CPSC 481/581: Introduction to Machine Learning (Fall 2021)
CPSC 661: Sampling Algorithms in Machine Learning (Spring 2021)


RESEARCH GROUP

Jane H. Lee
Siddharth Mitra
Kaylee (Yingxi) Yang

RESEARCH ALUMNI

Jun-Kun Wang (postdoc 2021-2023, now at UCSD)
Jiaming Liang (postdoc 2022-2023, now at University of Rochester)


RESEARCH

For complete publication list, please see Google Scholar

A symplectic analysis of alternating mirror descent
Jonas Katona, Xiuyuan Wang, Andre Wibisono
arXiv preprint arXiv:2405.03472, 2024
On independent samples along the Langevin diffusion and the Unadjusted Langevin Algorithm
Jiaming Liang, Siddharth Mitra, Andre Wibisono
arXiv preprint arXiv:2402.17067, 2024
Optimal score estimation via empirical Bayes smoothing
Andre Wibisono, Yihong Wu, Kaylee Yingxi Yang
COLT (Conference on Learning Theory) 2024
Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin Algorithm
Vishwak Srinivasan, Andre Wibisono, Ashia Wilson
COLT (Conference on Learning Theory) 2024
Extragradient Type Methods for Riemannian Variational Inequality Problems
Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob Abernethy, Molei Tao
AISTATS (Artificial Intelligence and Statistics) 2024
Learning Exponential Families from Truncated Samples
Jane Lee, Andre Wibisono, Manolis Zampetakis
NeurIPS (Neural Information Processing Systems) 2023
On a Class of Gibbs Sampling over Networks
Bo Yuan, Jiaojiao Fan, Jiaming Liang, Andre Wibisono, Yongxin Chen
COLT (Conference on Learning Theory) 2023
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation
Jun-Kun Wang, Andre Wibisono
ICLR (International Conference on Learning Representations) 2023
Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time
Jun-Kun Wang, Andre Wibisono
ICLR (International Conference on Learning Representations) 2023
Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization
Jun-Kun Wang, Andre Wibisono
ICLR (International Conference on Learning Representations) 2023
Convergence in KL Divergence of the Inexact Langevin Algorithm with Application to Score-based Generative Models
Kaylee Yingxi Yang, Andre Wibisono
arXiv preprint arXiv:2211.01512, 2022
Alternating Mirror Descent for Constrained Min-Max Games
Andre Wibisono, Molei Tao, Georgios Piliouras
NeurIPS (Neural Information Processing Systems) 2022
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Lojasiewicz Functions when the Non-Convexity is Averaged-Out
Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono, Bin Hu
ICML (International Conference on Machine Learning) 2022
Improved analysis for a proximal algorithm for sampling
Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono
COLT (Conference on Learning Theory) 2022
The Mirror Langevin Algorithm Converges with Vanishing Bias
Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono
ALT (Algorithmic Learning Theory) 2022
Last-iterate convergence rates for min-max optimization
Jacob Abernethy, Kevin Lai, and Andre Wibisono
ALT (Algorithmic Learning Theory) 2021
Fast Convergence of Fictitious Play for Diagonal Payoff Matrices
Jacob Abernethy, Kevin Lai, and Andre Wibisono
SODA (Symposium on Discrete Algorithms) 2021
Proximal Langevin Algorithm: Rapid convergence under isoperimetry
Andre Wibisono
arXiv preprint arXiv:1911.01469, 2019
Rapid convergence of the Unadjusted Langevin Algorithm: Isoperimetry suffices
Santosh Vempala and Andre Wibisono
NeurIPS (Neural Information Processing System) 2019
arXiv version | poster
Accelerating Rescaled Gradient Descent: Fast optimization of smooth functions
Ashia Wilson, Lester Mackey, and Andre Wibisono
NeurIPS (Neural Information Processing System) 2019
Convexity of mutual information along the Ornstein-Uhlenbeck flow
Andre Wibisono and Varun Jog
ISITA (International Symposium on Information Theory and Applications) 2018
Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
Andre Wibisono
COLT (Conference on Learning Theory) 2018
Convexity of mutual information along the heat flow
Andre Wibisono and Varun Jog
ISIT (International Symposium on Information Theory) 2018
Information and estimation in Fokker-Planck channels
Andre Wibisono, Varun Jog, and Po-Ling Loh
ISIT (International Symposium on Information Theory) 2017
A variational perspective on accelerated methods in optimization
Andre Wibisono, Ashia Wilson, and Michael Jordan
Proceedings of the National Academy of Sciences, 133, E7351--E7358, 2016. [arXiv version]
Optimal rates for zero-order convex optimization: the power of two function evaluations
John Duchi, Michael Jordan, Martin Wainwright, and Andre Wibisono
IEEE Transactions on Information Theory, 61(5): 2788--2806, May 2015
A Hadamard-type lower bound for symmetric diagonally dominant positive matrices
Christopher Hillar and Andre Wibisono
Linear Algebra and Applications, 472: 135--141, 2015
Convexity of reweighted Kikuchi approximation
Po-Ling Loh and Andre Wibisono
NIPS (Neural Information Processing System) 2014
How to hedge an option against an adversary: Black-Scholes pricing is minimax optimal
Jake Abernethy, Peter Bartlett, Rafael Frongillo, and Andre Wibisono
NIPS (Neural Information Processing System) 2013
Streaming variational Bayes
Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia Wilson, and Michael Jordan
NIPS (Neural Information Processing System) 2013
Maximum entropy distributions on graphs
Christopher Hillar and Andre Wibisono
arXiv preprint arXiv:1301.3321, 2013
Inverses of symmetric, diagonally dominant positive matrices and applications
Christopher Hillar, Shaowei Lin, and Andre Wibisono
arXiv preprint arXiv:1203.6812, 2013
Finite sample convergence rates of zero-order stochastic optimization methods
John Duchi, Michael Jordan, Martin Wainwright, and Andre Wibisono
NIPS (Neural Information Processing System) 2012
Minimax option pricing meets Black-Scholes in the limit
Jacob Abernethy, Rafael Frongillo, and Andre Wibisono
STOC (Symposium on the Theory of Computing) 2012

THESES

Variational and Dynamical Perspectives on Learning and Optimization
PhD in Computer Science, University of California, Berkeley, May 2016
Maximum Entropy Distributions on Graphs
MA in Statistics, University of California, Berkeley, May 2013
Generalization and Properties of the Neural Response
MEng in Computer Science, Massachusetts Institute of Technology, June 2010