5 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Provable Subspace Identification of Nonlinear Multi-view CCA
Zhiwei Han, Stefan Matthes, Hao Shen
We investigate the identifiability of nonlinear canonical correlation analysis (CCA) in a multi-view setup, in which each view is generated by applying an unknown nonlinear map to…
Provable Affine Identifiability of Nonlinear CCA under Latent Distributional Priors
Zhiwei Han, Stefan Matthes, Hao Shen
In this work, we establish the sufficient conditions under which nonlinear Canonical Correlation Analysis (CCA) recovers ground-truth latent factors up to an affine transformation.…
Mechanistic Independence: A Principle for Identifiable Disentangled Representations
Stefan Matthes, Zhiwei Han, Hao Shen
Disentangled representations seek to recover latent factors of variation underlying observed data, yet their identifiability is still not fully understood. We introduce a unified f…
Towards a Unified Framework of Contrastive Learning for Disentangled Representations
Stefan Matthes, Zhiwei Han, Hao Shen
Contrastive learning has recently emerged as a promising approach for learning data representations that discover and disentangle the explanatory factors of the data. Previous anal…
Metapath- and Entity-aware Graph Neural Network for Recommendation
Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy +6
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node conn…