Conditional distribution alignment

Distribution Alignment

Conditional distribution alignment

Distribution Alignment

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.

Avatar
David I. Inouye
Assistant Professor

My research interests include distribution alignment, localized learning, and explainable AI.

Publications

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned …

The unsupervised task of aligning two or more distributions in a shared latent space has many applications including fair …