Indeterminacy in Generative Models: Characterization and Strong Identifiability

2 June 2022

Papers citing "Indeterminacy in Generative Models: Characterization and Strong Identifiability"

8 / 8 papers shown

Title
Robustness of Nonlinear Representation Learning Simon Buchholz Bernhard Schölkopf OOD 143 3 0 19 Mar 2025
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling Emanuele Marconato Sébastien Lachapelle Sebastian Weichwald Luigi Gresele 69 3 0 30 Oct 2024
Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling Stathis Megas Daniel G. Chen K. Polański Moshe Eliasof Carola-Bibiane Schönlieb Sarah Teichmann 20 2 0 09 Sep 2024
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution Elen Vardanyan Sona Hunanyan T. Galstyan A. Minasyan A. Dalalyan 28 2 0 31 Jul 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen M. Besserve Wendong Liang Luigi Gresele Armin Kekić Elias Bareinboim David M. Blei Bernhard Schölkopf CML 18 56 0 01 Jun 2023
Posterior Collapse and Latent Variable Non-identifiability Yixin Wang David M. Blei John P. Cunningham CML DRL 83 71 0 02 Jan 2023
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 178 313 0 07 Feb 2020
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Jan Stühmer Richard E. Turner Sebastian Nowozin DRL BDL CoGe 111 25 0 05 Sep 2019