Robust Data-Driven Accelerated Mirror Descent. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Andreas Hauptmann; Carola-Bibiane Schönlieb (ICASSP, 2023)

Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals. - Hong Ye Tan; Stanley J. Osher; Wuchen Li (CoRR, 2023)

Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging. - Marcello Carioni; Subhadip Mukherjee; Hong Ye Tan; Junqi Tang (CoRR, 2023)

Data-Driven Mirror Descent with Input-Convex Neural Networks. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (SIAM J. Math. Data Sci., 2023)

Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (CoRR, 2024)

Provably Convergent Plug-and-Play Quasi-Newton Methods. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (SIAM J. Imaging Sci., 2024)

Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation. - Hong Ye Tan; Ziruo Cai; Marcelo Pereyra; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (Trans. Mach. Learn. Res., 2024)

Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration. - Hong Ye Tan; Subhadip Mukherjee; Junqi Tang; Carola-Bibiane Schönlieb (Trans. Mach. Learn. Res., 2024)

Dataset Distillation as Pushforward Optimal Quantization. - Hong Ye Tan; Emma Slade (CoRR, 2025)