3 papers
cs.LG2026
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang
Practitioners often face the challenge of deploying prediction models in new environments with shifted distributions of covariates and responses. With observational data, such shif…
stat.ML2026
Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
Tengyuan Liang, Kulunu Dharmakeerthi, Takuya Koriyama
Adding noise is easy; what about denoising? Diffusion is easy; what about reverting a diffusion? Diffusion-based generative models aim to denoise a Langevin diffusion chain, moving…
stat.ML2025
Beyond Linear Diffusions: Improved Representations for Rare Conditional Generative Modeling
Kulunu Dharmakeerthi, Yousef El-Laham, Henry H. Wong +3
Diffusion models have emerged as powerful generative frameworks with widespread applications across machine learning and artificial intelligence systems. While current research has…