| 2026 | COLT | The Geometry of Efficient Nonconvex Sampling. | Santosh S. Vempala, Andre Wibisono |
| 2026 | COLT | Accelerated Convex Optimization via Hamiltonian Dynamics with Deterministic Integration Time. | Xiuyuan Wang, Vishwak Srinivasan, Qiang Fu, Siddharth Mitra, Andre Wibisono, Ashia Wilson |
| 2025 | ALT | Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. | Siddharth Mitra, Andre Wibisono |
| 2025 | ALT | High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. | Vishwak Srinivasan, Andre Wibisono, Ashia Wilson |
| 2025 | COLT | On the Convergence of Min-Max Langevin Dynamics and Algorithm. | Yang Cai, Siddharth Mitra, Xiuyuan Wang, Andre Wibisono |
| 2025 | COLT | Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play. | John Lazarsfeld, Georgios Piliouras, Ryann Sim, Andre Wibisono |
| 2025 | COLT | Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract). | Jiaming Liang, Siddharth Mitra, Andre Wibisono |
| 2025 | COLT | Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract). | Andre Wibisono |
| 2024 | AISTATS | Extragradient Type Methods for Riemannian Variational Inequality Problems. | Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao |
| 2024 | COLT | Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm. | Vishwak Srinivasan, Andre Wibisono, Ashia C. Wilson |
| 2024 | COLT | Optimal score estimation via empirical Bayes smoothing. | Andre Wibisono, Yihong Wu, Kaylee Yingxi Yang |
| 2023 | COLT | On a Class of Gibbs Sampling over Networks. | Bo Yuan, Jiaojiao Fan, Jiaming Liang, Andre Wibisono, Yongxin Chen |
| 2023 | ICLR | Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization. | Jun-Kun Wang, Andre Wibisono |
| 2023 | ICLR | Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation. | Jun-Kun Wang, Andre Wibisono |
| 2023 | ICLR | Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time. | Jun-Kun Wang, Andre Wibisono |
| 2022 | ALT | The Mirror Langevin Algorithm Converges with Vanishing Bias. | Ruilin Li, Molei Tao, Santosh S. Vempala, Andre Wibisono |
| 2022 | COLT | Improved analysis for a proximal algorithm for sampling. | Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono |
| 2022 | ICML | 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 |
| 2021 | ALT | Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. | Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono |
| 2021 | SODA | Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. | Jacob D. Abernethy, Kevin A. Lai, Andre Wibisono |
| 2018 | COLT | Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem. | Andre Wibisono |
| 2018 | ISIT | Convexity of Mutual Information Along the Heat Flow. | Andre Wibisono, Varun S. Jog |
| 2018 | ISITA | Convexity of mutual information along the Ornstein-Uhlenbeck flow. | Andre Wibisono, Varun S. Jog |
| 2017 | ISIT | Information and estimation in Fokker-Planck channels. | Andre Wibisono, Varun S. Jog, Po-Ling Loh |
| 2012 | STOC | Minimax option pricing meets black-scholes in the limit. | Jacob D. Abernethy, Rafael M. Frongillo, Andre Wibisono |