Cover Image

Representation Disentaglement via Regularization by Causal Identification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Castorena, Juan
Format:	Preprint
Published:	2023
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Causally Inspired Regularization Enables Domain General Representations
by: Salaudeen, Olawale, et al.
Published: (2024)

On the Identification of Temporally Causal Representation with Instantaneous Dependence
by: Li, Zijian, et al.
Published: (2024)

Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
by: Komanduri, Aneesh, et al.
Published: (2023)

Implicit Causal Representation Learning via Switchable Mechanisms
by: Bagi, Shayan Shirahmad Gale, et al.
Published: (2024)

Adversary-Free Counterfactual Prediction via Information-Regularized Representations
by: Tang, Shiqin, et al.
Published: (2025)

Towards Unsupervised Causal Representation Learning via Latent Additive Noise Model Causal Autoencoders
by: Ong, Hans Jarett J., et al.
Published: (2025)

Jacobian Regularizer-based Neural Granger Causality
by: Zhou, Wanqi, et al.
Published: (2024)

Regularizing Extrapolation in Causal Inference
by: Arbour, David, et al.
Published: (2025)

Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
by: Kawakami, Yuta, et al.
Published: (2024)

Interventional Causal Representation Learning
by: Ahuja, Kartik, et al.
Published: (2022)

Efficiently Disentangle Causal Representations
by: Li, Yuanpeng, et al.
Published: (2022)

Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
by: Zhang, Wanpeng, et al.
Published: (2023)

Causal Representation Learning in Temporal Data via Single-Parent Decoding
by: Brouillard, Philippe, et al.
Published: (2024)

Causality-Inspired Robustness for Nonlinear Models via Representation Learning
by: Šola, Marin, et al.
Published: (2025)

Sample Complexity of Causal Identification with Temporal Heterogeneity
by: Rathod, Ameya, et al.
Published: (2026)

Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
by: Yang, Yupei, et al.
Published: (2024)

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

$\boldsymbolλ$-Orthogonality Regularization for Compatible Representation Learning
by: Ricci, Simone, et al.
Published: (2025)

Discrete Causal Representation Learning
by: Zhang, Wenjin, et al.
Published: (2026)

Improving Hyperbolic Representations via Gromov-Wasserstein Regularization
by: Yang, Yifei, et al.
Published: (2024)

CausalCOMRL: Context-Based Offline Meta-Reinforcement Learning with Causal Representation
by: Zhang, Zhengzhe, et al.
Published: (2025)

Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learning
by: Cai, Ruichu, et al.
Published: (2025)

Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction
by: Bhasker, Nithya, et al.
Published: (2026)

Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
by: Li, Jiahong, et al.
Published: (2025)

Comparing Two Proxy Methods for Causal Identification
by: Guo, Helen, et al.
Published: (2025)

General Identifiability and Achievability for Causal Representation Learning
by: Varıcı, Burak, et al.
Published: (2023)

Consistency of Neural Causal Partial Identification
by: Tan, Jiyuan, et al.
Published: (2024)

Spectral Representation for Causal Estimation with Hidden Confounders
by: Sun, Haotian, et al.
Published: (2024)

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

Orthogonal Representation Learning for Estimating Causal Quantities
by: Melnychuk, Valentyn, et al.
Published: (2025)

Causal Abstraction Inference under Lossy Representations
by: Xia, Kevin, et al.
Published: (2025)

Latent Manifold Reconstruction and Representation with Topological and Geometrical Regularization
by: Wang, Ren, et al.
Published: (2025)

ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
by: Kulkarni, Pranamya, et al.
Published: (2025)

Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing
by: Nguyen, Minh, et al.
Published: (2024)

SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
by: Fu, Rong, et al.
Published: (2026)

Identification of Causal Direction under an Arbitrary Number of Latent Confounders
by: Chen, Wei, et al.
Published: (2025)

Downlink Channel Covariance Matrix Estimation via Representation Learning with Graph Regularization
by: Zerin, Melih Can, et al.
Published: (2024)

A Fully Probabilistic Tensor Network for Regularized Volterra System Identification
by: Kilic, Afra, et al.
Published: (2025)

SSL Framework for Causal Inconsistency between Structures and Representations
by: Chen, Hang, et al.
Published: (2023)

Understanding Catastrophic Interference: On the Identifibility of Latent Representations
by: Li, Yuke, et al.
Published: (2025)