Cover Image

Multi-View Causal Representation Learning with Partial Observability

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yao, Dingling, Xu, Danru, Lachapelle, Sébastien, Magliacane, Sara, Taslakian, Perouz, Martius, Georg, von Kügelgen, Julius, Locatello, Francesco
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Sparsity Principle for Partially Observable Causal Representation Learning
by: Xu, Danru, et al.
Published: (2024)

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing
by: Xu, Danru, et al.
Published: (2026)

Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
by: von Kügelgen, Julius
Published: (2024)

Learning to Defer for Causal Discovery with Imperfect Experts
by: Clivio, Oscar, et al.
Published: (2025)

Marrying Causal Representation Learning with Dynamical Systems for Science
by: Yao, Dingling, et al.
Published: (2024)

Unifying Causal Representation Learning with the Invariance Principle
by: Yao, Dingling, et al.
Published: (2024)

The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
by: Yao, Dingling, et al.
Published: (2025)

Interaction Asymmetry: A General Principle for Learning Composable Abstractions
by: Brady, Jack, et al.
Published: (2024)

Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations
by: Wu, Bohan, et al.
Published: (2026)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)

Transferring Causal Effects using Proxies
by: Iglesias-Alonso, Manuel, et al.
Published: (2025)

Learning Causal Abstractions of Linear Structural Causal Models
by: Massidda, Riccardo, et al.
Published: (2024)

Local Causal Discovery for Statistically Efficient Causal Inference
by: Schubert, Mátyás, et al.
Published: (2025)

Towards the Reusability and Compositionality of Causal Representations
by: Talon, Davide, et al.
Published: (2024)

Causal Component Analysis
by: Wendong, Liang, et al.
Published: (2023)

From Pixels to Components: Eigenvector Masking for Visual Representation Learning
by: Bizeul, Alice, et al.
Published: (2025)

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
by: Schubert, Mátyás, et al.
Published: (2025)

Identifying General Mechanism Shifts in Linear Causal Representations
by: Chen, Tianyu, et al.
Published: (2024)

Combining Causal Models for More Accurate Abstractions of Neural Networks
by: Pîslar, Theodora-Mara, et al.
Published: (2025)

BiXSE: Improving Dense Retrieval via Probabilistic Graded Relevance Distillation
by: Tsirigotis, Christos, et al.
Published: (2025)

Exploratory Causal Inference in SAEnce
by: Mencattini, Tommaso, et al.
Published: (2025)

Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
by: Chen, Jiale, et al.
Published: (2024)

On the Identifiability of Latent Action Policies
by: Lachapelle, Sébastien
Published: (2025)

Causal Learning with the Invariance Principle
by: Montagna, Francesco, et al.
Published: (2026)

Towards Causal Representation Learning with Observable Sources as Auxiliaries
by: Kim, Kwonho, et al.
Published: (2025)

The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
by: Yao, Dingling, et al.
Published: (2026)

Causal Action Influence Aware Counterfactual Data Augmentation
by: Urpí, Núria Armengol, et al.
Published: (2024)

Partially Observable Gaussian Process Network and Doubly Stochastic Variational Inference
by: Kiroriwal, Saksham, et al.
Published: (2025)

Mem-$π$: Adaptive Memory through Learning When and What to Generate
by: Wang, Xiaoqiang, et al.
Published: (2026)

Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2023)

All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
by: Marconato, Emanuele, et al.
Published: (2024)

Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
by: Joshi, Shruti, et al.
Published: (2025)

Improving GUI Grounding with Explicit Position-to-Coordinate Mapping
by: Wang, Suyuchen, et al.
Published: (2025)

Mechanistic Neural Networks for Scientific Machine Learning
by: Pervez, Adeel, et al.
Published: (2024)

Towards a holistic understanding of Selection Bias for Causal Effect Identification
by: Qiu, Yiwen, et al.
Published: (2026)

Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints
by: Kolev, Pavel, et al.
Published: (2025)

Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
by: Luis, Carlos E., et al.
Published: (2024)

Learning 3D-Gaussian Simulators from RGB Videos
by: Zhobro, Mikel, et al.
Published: (2025)

Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
by: Gupta, Shubham, et al.
Published: (2025)

XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
by: Monteiro, João, et al.
Published: (2024)