Distribution alignment has the opposite objective of classification. While classification finds a representation that separates two distributions, alignment finds a representation that brings together two distributions. Alignment has been used to enhance robustness or enforce constraints in many recent machine learning applications including domain generalization, causal discovery, and fair representation learning. Despite these important applications, distribution alignment research lacks a unified and systematic conceptual framework and has primarily focused on GAN-based adversarial alignment for images. To address this gap, I will present a unifying alignment framework that encompasses alignment concepts, measures, algorithms, and applications. Specifically, I will formalize the definition of distribution alignment, develop novel non-adversarial alignment measures and algorithms, and discuss alignment applications in causal discovery and domain generalization. Ultimately, this work aims to advance the science of distribution alignment to enable the next generation of contextually aware and robust AI systems.
Conditional distribution alignment
Publications
Efficient Federated Domain Translation
A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically …
Cooperative Distribution Alignment via JSD Upper Bound
Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned …
Iterative Alignment Flows
The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair …