3 accepted papers
Multimodal alignment fails under deployment shift because standard objectives entangle cross-modal dependence with marginal-sensitive geometry. Stable Spectral Copula Alignment (SSCA) provides a deployment protocol targeting copula-stable dependence under strictly monotone marginal distortions, with…
This paper presents an optimized approach to enhance the computation of Hirschfeld-Gebelein-Rényi (HGR) maximal correlation, addressing computational and efficiency challenges in large-scale neural networks and multimodal learning. The UniFast HGR framework introduces three key innovations: replacin…
Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature, since MNAR data are ubiquitous in modern recommender systems. Instead, missing-at-random (MAR) data, namely randomized controlled trials (RCTs), are usually required by…