v1v2 (latest)

Identifiability of deep generative models without auxiliary information

Neural Information Processing Systems (NeurIPS), 2022

20 June 2022

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

48 / 48 papers shown

Title
Operationalizing Quantized Disentanglement Vitória Barin Pacela Kartik Ahuja Simon Lacoste-Julien Pascal Vincent 69 0 0 25 Nov 2025
Thought Communication in Multiagent Collaboration Yujia Zheng Zhuokai Zhao Zijian Li Yaqi Xie Mingze Gao Lizhu Zhang Kun Zhang AI4CE 125 2 0 23 Oct 2025
Diverse Influence Component Analysis: A Geometric Approach to Nonlinear Mixture Identifiability Hoang-Son Nguyen Xiao Fu CML 231 0 0 19 Oct 2025
Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models Ehsan Sharifian Saber Salehkaleybar Negar Kiyavash CML 110 0 0 25 Sep 2025
PMODE: Theoretically Grounded and Modular Mixture Modeling Robert A. Vandermeulen MoE 111 0 0 29 Aug 2025
Identifiability of Deep Polynomial Neural Networks K. Usevich Clara Dérand Ricardo Augusto Borsoi Marianne Clausel 183 3 0 20 Jun 2025
Identifiable Object Representations under Spatial Ambiguities Avinash Kori Francesca Toni Ben Glocker OCL 170 0 0 09 Jun 2025
Causality-Inspired Robustness for Nonlinear Models via Representation Learning Marin Šola Peter Bühlmann Xinwei Shen OOD 246 2 0 19 May 2025
Robustness of Nonlinear Representation LearningInternational Conference on Machine Learning (ICML), 2025 Simon Buchholz Bernhard Schölkopf OOD 783 5 0 19 Mar 2025
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal TransportabilityCLEaR (CLEaR), 2025 Mingwei Deng Ville Kyrki Dominik Baumann 225 3 0 27 Feb 2025
Identifiability Guarantees for Causal Disentanglement from Purely Observational DataNeural Information Processing Systems (NeurIPS), 2024 Ryan Welch Jiaqi Zhang Caroline Uhler CML OOD 277 3 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 226 5 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 179 7 0 09 Oct 2024
Controlling for discrete unmeasured confounding in nonlinear causal modelsCLEaR (CLEaR), 2024 Patrick Burauel Frederick Eberhardt Michel Besserve CML 126 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 217 7 0 11 Jun 2024
Learning Discrete Concepts in Latent Hierarchical Models Lingjing Kong Guan-Hong Chen Erdun Gao Eric P. Xing Yuejie Chi Kun Zhang 332 12 0 01 Jun 2024
Marrying Causal Representation Learning with Dynamical Systems for Science Dingling Yao Caroline Muller Francesco Locatello CML AI4CE 371 18 0 22 May 2024
Distributional Principal Autoencoders Xinwei Shen N. Meinshausen 194 4 0 21 Apr 2024
Non-negative Contrastive Learning Yifei Wang Tao Gui Yaoyu Guo Yisen Wang 279 14 0 19 Mar 2024
A Sparsity Principle for Partially Observable Causal Representation LearningInternational Conference on Machine Learning (ICML), 2024 Danru Xu Dingling Yao Sébastien Lachapelle Perouz Taslakian Julius von Kügelgen Francesco Locatello Sara Magliacane CML 215 21 0 13 Mar 2024
On the Origins of Linear Representations in Large Language Models Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam Victor Veitch 233 48 0 06 Mar 2024
Separating common from salient patterns with Contrastive Representation Learning Robin Louiset Edouard Duchesnay Antoine Grigis Pietro Gori SSL DRL 220 3 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 374 28 0 14 Feb 2024
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook Wenzhao Jiang Hao Liu Hui Xiong CML AI4CE 475 9 0 19 Dec 2023
Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data Yuqin Yang Saber Salehkaleybar Negar Kiyavash CML 251 3 0 11 Dec 2023
Multi-View Causal Representation Learning with Partial ObservabilityInternational Conference on Learning Representations (ICLR), 2023 Dingling Yao Danru Xu Sébastien Lachapelle Sara Magliacane Perouz Taslakian Georg Martius Julius von Kügelgen Francesco Locatello CML 333 52 0 07 Nov 2023
Generalizing Nonlinear ICA Beyond Structural SparsityNeural Information Processing Systems (NeurIPS), 2023 Yujia Zheng Kun Zhang CML 146 25 0 01 Nov 2023
Causal Representation Learning Made Identifiable by Grouping of Observational VariablesInternational Conference on Machine Learning (ICML), 2023 H. Morioka Aapo Hyvarinen OOD CML BDL 362 19 0 24 Oct 2023
Identifying Representations for Intervention ExtrapolationInternational Conference on Learning Representations (ICLR), 2023 Sorawit Saengkyongam Ezgi Ozyilkan Pradeep Ravikumar Niklas Pfister Jonas Peters CML OOD 203 17 0 06 Oct 2023
Multi-Domain Causal Representation Learning via Weak Distributional InvariancesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023 Kartik Ahuja Amin Mansouri Yixin Wang CML OOD 313 16 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 309 0 0 01 Sep 2023
Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models Reza Sameni 130 2 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 277 2 0 11 Jul 2023
On the Identifiability of Quantized FactorsCLEaR (CLEaR), 2023 Vitória Barin Pacela Kartik Ahuja Damien Scieur Pascal Vincent OOD CML 242 3 0 28 Jun 2023
Leveraging Task Structures for Improved Identifiability in Neural Network Representations Jiajun He Julien Horwood Juyeon Heo José Miguel Hernández-Lobato CML 248 1 0 26 Jun 2023
Learning Linear Causal Representations from Interventions under General Nonlinear MixingNeural Information Processing Systems (NeurIPS), 2023 Simon Buchholz Goutham Rajendran Elan Rosenfeld Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar CML 310 76 0 04 Jun 2023
Nonparametric Identifiability of Causal Representations from Unknown InterventionsNeural Information Processing Systems (NeurIPS), 2023 Julius von Kügelgen M. Besserve Wendong Liang Luigi Gresele Armin Kekić Elias Bareinboim David M. Blei Bernhard Schölkopf CML 443 80 0 01 Jun 2023
Neuro-Causal Factor Analysis Alex Markham Ming-Yuan Liu Bryon Aragam Liam Solus CML 155 5 0 31 May 2023
Disentanglement via Latent QuantizationNeural Information Processing Systems (NeurIPS), 2023 Kyle Hsu W. Dorrell James C. R. Whittington Jiajun Wu Chelsea Finn DRL 293 34 0 28 May 2023
Causal Component AnalysisNeural Information Processing Systems (NeurIPS), 2023 Wendong Liang Armin Kekić Julius von Kügelgen Simon Buchholz M. Besserve Luigi Gresele Bernhard Schölkopf CML 336 48 0 26 May 2023
On the Identifiability of Switching Dynamical SystemsInternational Conference on Machine Learning (ICML), 2023 Carles Balsells-Rodas Yixin Wang Yingzhen Li 314 7 0 25 May 2023
Manifold Learning by Mixture Models of VAEs for Inverse ProblemsJournal of machine learning research (JMLR), 2023 Giovanni S. Alberti J. Hertrich Matteo Santacesaria Silvia Sciutto DRL 324 11 0 27 Mar 2023
Causally Disentangled Generative Variational AutoEncoderEuropean Conference on Artificial Intelligence (ECAI), 2023 SeungHwan An Kyungwoo Song Jong-June Jeon OOD CoGe DRL CML 157 7 0 23 Feb 2023
Generative Causal Representation Learning for Out-of-Distribution Motion ForecastingInternational Conference on Machine Learning (ICML), 2023 Shayan Shirahmad Gale Bagi Zahra Gharaee Oliver Schulte Mark Crowley OODD OOD 234 17 0 17 Feb 2023
Emerging Synergies in Causality and Deep Generative Models: A Survey Guanglin Zhou Shaoan Xie Guang-Yuan Hao Shiming Chen Erdun Gao Xiwei Xu Chen Wang Liming Zhu Lina Yao Kun Zhang AI4CE 366 14 0 29 Jan 2023
Generalized Identifiability Bounds for Mixture Models with Grouped SamplesIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022 Robert A. Vandermeulen René Saitenmacher 158 4 0 22 Jul 2022
Tight Bounds on the Hardness of Learning Simple Nonparametric MixturesAnnual Conference Computational Learning Theory (COLT), 2022 Bryon Aragam W. Tai 363 3 0 28 Mar 2022
Uniform Consistency in Nonparametric Mixture ModelsAnnals of Statistics (Ann. Stat.), 2021 Bryon Aragam Ruiyi Yang 249 6 0 31 Aug 2021