Identifiability of deep generative models without auxiliary information

20 June 2022

Papers citing "Identifiability of deep generative models without auxiliary information"

43 / 43 papers shown

Title
Robustness of Nonlinear Representation Learning Simon Buchholz Bernhard Schölkopf OOD 137 3 0 19 Mar 2025
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability Mingwei Deng Ville Kyrki Dominik Baumann 36 0 0 27 Feb 2025
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data Ryan Welch Jiaqi Zhang Caroline Uhler CML OOD 51 1 0 31 Oct 2024
Language Agents Meet Causality -- Bridging LLMs and Causal World Models John Gkountouras Matthias Lindemann Phillip Lippe E. Gavves Ivan Titov LRM 28 0 0 25 Oct 2024
Causal Representation Learning in Temporal Data via Single-Parent Decoding Philippe Brouillard Sébastien Lachapelle Julia Kaltenborn Yaniv Gurwicz Dhanya Sridhar Alexandre Drouin Peer Nowack Jakob Runge David Rolnick CML 32 3 0 09 Oct 2024
Controlling for discrete unmeasured confounding in nonlinear causal models Patrick Burauel Frederick Eberhardt Michel Besserve CML 23 0 0 10 Aug 2024
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention Avinash Kori Francesco Locatello Ainkaran Santhirasekaram Francesca Toni Ben Glocker Fabio De Sousa Ribeiro OCL 45 1 0 11 Jun 2024
Learning Discrete Concepts in Latent Hierarchical Models Lingjing Kong Guan-Hong Chen Biwei Huang Eric P. Xing Yuejie Chi Kun Zhang 52 4 0 01 Jun 2024
Marrying Causal Representation Learning with Dynamical Systems for Science Dingling Yao Caroline Muller Francesco Locatello CML AI4CE 40 6 0 22 May 2024
Distributional Principal Autoencoders Xinwei Shen N. Meinshausen 21 2 0 21 Apr 2024
Non-negative Contrastive Learning Yifei Wang Qi Zhang Yaoyu Guo Yisen Wang 26 9 0 19 Mar 2024
A Sparsity Principle for Partially Observable Causal Representation Learning Danru Xu Dingling Yao Sébastien Lachapelle Perouz Taslakian Julius von Kügelgen Francesco Locatello Sara Magliacane CML 37 13 0 13 Mar 2024
On the Origins of Linear Representations in Large Language Models Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam Victor Veitch 61 24 0 06 Mar 2024
Separating common from salient patterns with Contrastive Representation Learning Robin Louiset Edouard Duchesnay Antoine Grigis Pietro Gori SSL DRL 38 1 0 19 Feb 2024
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models Goutham Rajendran Simon Buchholz Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar AI4CE 91 21 0 14 Feb 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 38 2 0 19 Dec 2023
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data Yuqin Yang Saber Salehkaleybar Negar Kiyavash CML 20 1 0 11 Dec 2023
Multi-View Causal Representation Learning with Partial Observability Dingling Yao Danru Xu Sébastien Lachapelle Sara Magliacane Perouz Taslakian Georg Martius Julius von Kügelgen Francesco Locatello CML 42 30 0 07 Nov 2023
Generalizing Nonlinear ICA Beyond Structural Sparsity Yujia Zheng Kun Zhang CML 21 16 0 01 Nov 2023
Causal Representation Learning Made Identifiable by Grouping of Observational Variables H. Morioka Aapo Hyvarinen OOD CML BDL 30 9 0 24 Oct 2023
Identifying Representations for Intervention Extrapolation Sorawit Saengkyongam Ezgi Ozyilkan Pradeep Ravikumar Niklas Pfister Jonas Peters CML OOD 16 14 0 06 Oct 2023
Multi-Domain Causal Representation Learning via Weak Distributional Invariances Kartik Ahuja Amin Mansouri Yixin Wang CML OOD 21 10 0 04 Oct 2023
Learning multi-modal generative models with permutation-invariant encoders and tighter variational bounds Marcel Hirt Domenico Campolo Victoria Leong Juan-Pablo Ortega DRL 8 0 0 01 Sep 2023
Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models Reza Sameni 19 1 0 21 Jul 2023
A Causal Ordering Prior for Unsupervised Representation Learning Avinash Kori Pedro Sanchez Konstantinos Vilouras Ben Glocker Sotirios A. Tsaftaris BDL SSL CML 32 0 0 11 Jul 2023
On the Identifiability of Quantized Factors Vitória Barin Pacela Kartik Ahuja Simon Lacoste-Julien Pascal Vincent OOD CML 21 1 0 28 Jun 2023
Leveraging Task Structures for Improved Identifiability in Neural Network Representations Wenlin Chen Julien Horwood Juyeon Heo José Miguel Hernández-Lobato CML 30 1 0 26 Jun 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz Goutham Rajendran Elan Rosenfeld Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar CML 34 57 0 04 Jun 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
Neuro-Causal Factor Analysis Alex Markham Ming-Yu Liu Bryon Aragam Liam Solus CML 23 3 0 31 May 2023
Disentanglement via Latent Quantization Kyle Hsu W. Dorrell James C. R. Whittington Jiajun Wu Chelsea Finn DRL 23 24 0 28 May 2023
Causal Component Analysis Wendong Liang Armin Kekić Julius von Kügelgen Simon Buchholz M. Besserve Luigi Gresele Bernhard Schölkopf CML 29 36 0 26 May 2023
On the Identifiability of Switching Dynamical Systems Carles Balsells-Rodas Yixin Wang Yingzhen Li 32 4 0 25 May 2023
Manifold Learning by Mixture Models of VAEs for Inverse Problems Giovanni S. Alberti J. Hertrich Matteo Santacesaria Silvia Sciutto DRL 29 6 0 27 Mar 2023
Causally Disentangled Generative Variational AutoEncoder SeungHwan An Kyungwoo Song Jong-June Jeon OOD CoGe DRL CML 19 4 0 23 Feb 2023
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting Shayan Shirahmad Gale Bagi Zahra Gharaee Oliver Schulte Mark Crowley OODD OOD 21 12 0 17 Feb 2023
Emerging Synergies in Causality and Deep Generative Models: A Survey Guanglin Zhou Shaoan Xie Guang-Yuan Hao Shiming Chen Biwei Huang Xiwei Xu Chen Wang Liming Zhu Lina Yao Kun Zhang AI4CE 50 11 0 29 Jan 2023
Posterior Collapse and Latent Variable Non-identifiability Yixin Wang David M. Blei John P. Cunningham CML DRL 83 71 0 02 Jan 2023
Generalized Identifiability Bounds for Mixture Models with Grouped Samples Robert A. Vandermeulen René Saitenmacher 20 2 0 22 Jul 2022
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures Bryon Aragam W. Tai 17 3 0 28 Mar 2022
Uniform Consistency in Nonparametric Mixture Models Bryon Aragam Ruiyi Yang 18 6 0 31 Aug 2021
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 238 207 0 17 Feb 2021
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 175 313 0 07 Feb 2020