Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (NIPS, 2012)

Minimax option pricing meets black-scholes in the limit. - Jacob D. Abernethy; Rafael M. Frongillo; Andre Wibisono (STOC, 2012)

How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal. - Jacob D. Abernethy; Peter L. Bartlett; Rafael M. Frongillo; Andre Wibisono (NIPS, 2013)

Streaming Variational Bayes. - Tamara Broderick; Nicholas Boyd; Andre Wibisono; Ashia C. Wilson; Michael I. Jordan (NIPS, 2013)

Optimal rates for zero-order optimization: the power of two function evaluations. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (CoRR, 2013)

Concavity of reweighted Kikuchi approximation. - Po-Ling Loh; Andre Wibisono (NIPS, 2014)

Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations. - John C. Duchi; Michael I. Jordan; Martin J. Wainwright; Andre Wibisono (IEEE Trans. Inf. Theory, 2015)

A Variational Perspective on Accelerated Methods in Optimization. - Andre Wibisono; Ashia C. Wilson; Michael I. Jordan (CoRR, 2016)

Variational and Dynamical Perspectives On Learning and Optimization. - Andre Wibisono (2016)

Information and estimation in Fokker-Planck channels. - Andre Wibisono; Varun S. Jog; Po-Ling Loh (ISIT, 2017)

Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem. - Andre Wibisono (COLT, 2018)

Convexity of Mutual Information Along the Heat Flow. - Andre Wibisono; Varun S. Jog (ISIT, 2018)

Convexity of mutual information along the Ornstein-Uhlenbeck flow. - Andre Wibisono; Varun S. Jog (ISITA, 2018)

Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices. - Santosh S. Vempala; Andre Wibisono (NeurIPS, 2019)

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. - Ashia C. Wilson; Lester Mackey; Andre Wibisono (NeurIPS, 2019)

Rapid Convergence of the Unadjusted Langevin Algorithm: Log-Sobolev Suffices. - Santosh S. Vempala; Andre Wibisono (CoRR, 2019)

Last-iterate convergence rates for min-max optimization. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (CoRR, 2019)

Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry. - Andre Wibisono (CoRR, 2019)

Fictitious Play: Convergence, Smoothness, and Optimism. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (CoRR, 2019)

Last-Iterate Convergence Rates for Min-Max Optimization: Convergence of Hamiltonian Gradient Descent and Consensus Optimization. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (ALT, 2021)

Fast Convergence of Fictitious Play for Diagonal Payoff Matrices. - Jacob D. Abernethy; Kevin A. Lai; Andre Wibisono (SODA, 2021)

The Mirror Langevin Algorithm Converges with Vanishing Bias. - Ruilin Li; Molei Tao; Santosh S. Vempala; Andre Wibisono (ALT, 2022)

Improved analysis for a proximal algorithm for sampling. - Yongxin Chen; Sinho Chewi; Adil Salim; Andre Wibisono (COLT, 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, 2022)

Aggregation in the Mirror Space (AIMS): Fast, Accurate Distributed Machine Learning in Military Settings. - Ryan Yang; Haizhou Du; Andre Wibisono; Patrick Baker (MILCOM, 2022)

Alternating Mirror Descent for Constrained Min-Max Games. - Andre Wibisono; Molei Tao; Georgios Piliouras (NeurIPS, 2022)

Achieving Efficient Distributed Machine Learning Using a Novel Non-Linear Class of Aggregation Functions. - Haizhou Du; Ryan Yang; Yijian Chen; Qiao Xiang; Andre Wibisono; Wei Huang (CoRR, 2022)

Convergence in KL Divergence of the Inexact Langevin Algorithm with Application to Score-based Generative Models. - Andre Wibisono; Kaylee Yingxi Yang (CoRR, 2022)

On a Class of Gibbs Sampling over Networks. - Bo Yuan; Jiaojiao Fan; Jiaming Liang; Andre Wibisono; Yongxin Chen (COLT, 2023)

Continuized Acceleration for Quasar Convex Functions in Non-Convex Optimization. - Jun-Kun Wang; Andre Wibisono (ICLR, 2023)

Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation. - Jun-Kun Wang; Andre Wibisono (ICLR, 2023)

Accelerating Hamiltonian Monte Carlo via Chebyshev Integration Time. - Jun-Kun Wang; Andre Wibisono (ICLR, 2023)

Learning Exponential Families from Truncated Samples. - Jane H. Lee; Andre Wibisono; Emmanouil Zampetakis (NeurIPS, 2023)

Mitigating Catastrophic Forgetting in Long Short-Term Memory Networks. - Ketaki Joshi; Raghavendra Pradyumna Pothukuchi; Andre Wibisono; Abhishek Bhattacharjee (CoRR, 2023)

Extragradient Type Methods for Riemannian Variational Inequality Problems. - Zihao Hu; Guanghui Wang; Xi Wang; Andre Wibisono; Jacob D. Abernethy; Molei Tao (AISTATS, 2024)

Fast sampling from constrained spaces using the Metropolis-adjusted Mirror Langevin algorithm. - Vishwak Srinivasan; Andre Wibisono; Ashia C. Wilson (COLT, 2024)

Optimal score estimation via empirical Bayes smoothing. - Andre Wibisono; Yihong Wu; Kaylee Yingxi Yang (COLT, 2024)

On Independent Samples Along the Langevin Diffusion and the Unadjusted Langevin Algorithm. - Jiaming Liang; Siddharth Mitra; Andre Wibisono (CoRR, 2024)

A Symplectic Analysis of Alternating Mirror Descent. - Jonas Katona; Xiuyuan Wang 007; Andre Wibisono (CoRR, 2024)

Fast Convergence of Ψ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. - Siddharth Mitra; Andre Wibisono (CoRR, 2024)

Convergence of the Min-Max Langevin Dynamics and Algorithm for Zero-Sum Games. - Yang Cai; Siddharth Mitra; Xiuyuan Wang; Andre Wibisono (CoRR, 2024)

Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. - Siddharth Mitra; Andre Wibisono (ALT, 2025)

High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. - Vishwak Srinivasan; Andre Wibisono; Ashia Wilson (ALT, 2025)

On the Convergence of Min-Max Langevin Dynamics and Algorithm. - Yang Cai 001; Siddharth Mitra; Xiuyuan Wang; Andre Wibisono (COLT, 2025)

Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play. - John Lazarsfeld; Georgios Piliouras; Ryann Sim; Andre Wibisono (COLT, 2025)

Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract). - Jiaming Liang; Siddharth Mitra; Andre Wibisono (COLT, 2025)

Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract). - Andre Wibisono (COLT, 2025)

Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality. - Andre Wibisono (CoRR, 2025)

Hamiltonian Descent Algorithms for Optimization: Accelerated Rates via Randomized Integration Time. - Qiang Fu; Andre Wibisono (CoRR, 2025)