{"total":52,"rows":[{"id":"2306.02235","title":"Learning Linear Causal Representations from Interventions under General Nonlinear Mixing","created":"2023-06-04","cited_by_count":92,"area":null},{"id":"2403.03867","title":"On the Origins of Linear Representations in Large Language Models","created":"2024-03-06","cited_by_count":79,"area":null},{"id":"2305.17225","title":"Causal Component Analysis","created":"2023-05-26","cited_by_count":56,"area":null},{"id":"2303.16535","title":"Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning","created":"2023-03-29","cited_by_count":48,"area":null},{"id":"2310.04723","title":"Subspace Identification for Multi-Source Domain Adaptation","created":"2023-10-07","cited_by_count":37,"area":null},{"id":"2410.21869","title":"Cross-Entropy Is All You Need To Invert the Data Generating Process","created":"2024-10-29","cited_by_count":31,"area":null},{"id":"2402.09236","title":"Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models","created":"2024-02-14","cited_by_count":29,"area":null},{"id":"2604.21691","title":"There Will Be a Scientific Theory of Deep Learning","created":"2026-04-23","cited_by_count":29,"area":null},{"id":"2405.01964","title":"Position: Understanding LLMs Requires More Than Statistical Generalization","created":"2024-05-03","cited_by_count":21,"area":null},{"id":"2407.09468","title":"Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures","created":"2024-07-12","cited_by_count":20,"area":null},{"id":"2405.15325","title":"On the Identification of Temporally Causal Representation with Instantaneous Dependence","created":"2024-05-24","cited_by_count":16,"area":null},{"id":"2406.14302","title":"Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning","created":"2024-06-20","cited_by_count":16,"area":null},{"id":"2503.01824","title":"From superposition to sparse codes: interpretable representations in neural networks","created":"2025-03-03","cited_by_count":16,"area":null},{"id":"2506.17093","title":"Identifiability of Deep Polynomial Neural Networks","created":"2025-06-20","cited_by_count":12,"area":null},{"id":"2311.04837","title":"Identifying Semantic Component for Robust Molecular Property Prediction","created":"2023-11-08","cited_by_count":10,"area":null},{"id":"2310.07665","title":"Deep Backtracking Counterfactuals for Causally Compliant Explanations","created":"2023-10-11","cited_by_count":9,"area":null},{"id":"2310.11431","title":"Identifying Interpretable Visual Features in Artificial and Biological Neural Systems","created":"2023-10-17","cited_by_count":9,"area":null},{"id":"2311.18048","title":"An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis","created":"2023-11-29","cited_by_count":9,"area":null},{"id":"2410.04120","title":"Rethinking Fair Representation Learning for Performance-Sensitive Tasks","created":"2024-10-05","cited_by_count":8,"area":null},{"id":"2312.13438","title":"Independent Mechanism Analysis and the Manifold Hypothesis","created":"2023-12-20","cited_by_count":7,"area":null},{"id":"2406.02049","title":"Causal Effect Identification in LiNGAM Models with Latent Confounders","created":"2024-06-04","cited_by_count":7,"area":null},{"id":"2503.15355","title":"Robustness of Nonlinear Representation Learning","created":"2025-03-19","cited_by_count":7,"area":null},{"id":"2502.06536","title":"Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions","created":"2025-02-10","cited_by_count":6,"area":null},{"id":"2510.08294","title":"Counterfactual Identifiability via Dynamic Optimal Transport","created":"2025-10-09","cited_by_count":6,"area":null},{"id":"2405.16083","title":"From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals","created":"2024-05-25","cited_by_count":4,"area":null},{"id":"2503.00639","title":"Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning","created":"2025-03-01","cited_by_count":4,"area":null},{"id":"2506.03784","title":"When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective","created":"2025-06-04","cited_by_count":4,"area":null},{"id":"2307.05704","title":"A Causal Ordering Prior for Unsupervised Representation Learning","created":"2023-07-11","cited_by_count":3,"area":null},{"id":"2502.12603","title":"Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2507.18838","title":"Flow Stochastic Segmentation Networks","created":"2025-07-24","cited_by_count":3,"area":null},{"id":"2510.00136","title":"Nonparametric Identification of Latent Concepts","created":"2025-09-30","cited_by_count":3,"area":null},{"id":"2605.20396","title":"Score-Based Causal Discovery of Latent Variable Causal Models","created":"2026-05-19","cited_by_count":3,"area":null},{"id":"2402.12767","title":"Nonstationary Time Series Forecasting via Unknown Distribution Adaptation","created":"2024-02-20","cited_by_count":2,"area":null},{"id":"2410.19426","title":"Analyzing Generative Models by Manifold Entropic Metrics","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2510.18281","title":"Online Time Series Forecasting with Theoretical Guarantees","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2512.23408","title":"Probabilistic Modelling is Sufficient for Causal Inference","created":"2025-12-29","cited_by_count":2,"area":null},{"id":"2502.16637","title":"Time Series Domain Adaptation via Latent Invariant Causal Mechanism","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2505.07180","title":"Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism","created":"2025-05-12","cited_by_count":1,"area":null},{"id":"2509.04295","title":"A Primer on Causal and Statistical Dataset Biases for Fair and Robust Image Analysis","created":"2025-09-04","cited_by_count":1,"area":null},{"id":"2602.24275","title":"Hierarchical Action Learning for Weakly-Supervised Action Segmentation","created":"2026-02-27","cited_by_count":1,"area":null},{"id":"2604.13218","title":"Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing","created":"2026-04-14","cited_by_count":1,"area":null},{"id":"2605.14927","title":"Learning with Shallow Neural Networks on Cluster-Structured Features","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2402.15819","title":"Debiased Model-based Interactive Recommendation","created":"2024-02-24","cited_by_count":0,"area":null},{"id":"2506.02200","title":"Learning Treatment Representations for Downstream Instrumental Variable Regression","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2507.05412","title":"Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2510.16026","title":"A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data","created":"2025-10-15","cited_by_count":0,"area":null},{"id":"2603.09288","title":"Proxy-Guided Measurement Calibration","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.17568","title":"Diverse Dictionary Learning","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.13589","title":"Causal Learning with the Invariance Principle","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2606.17967","title":"Learning task-specific subspaces via interventional post-training of speech foundation models","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2606.21385","title":"Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2609.21926","title":"Kinks vs. Smoothness: Identifiability of Real Analytic nICA for Laplace-like Sources","created":"2026-09-18","cited_by_count":0,"area":null}],"next_cursor":null}