Beyond identifiability: Learning causal representations with few environments and finite samples
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Inbeom, Jin, Tongtong, Aragam, Bryon |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024)
by: Rajendran, Goutham, et al.
Published: (2024)
Greedy equivalence search for nonparametric graphical models
by: Aragam, Bryon
Published: (2024)
by: Aragam, Bryon
Published: (2024)
Differentiable Structure Learning and Causal Discovery for General Binary Data
by: Deng, Chang, et al.
Published: (2025)
by: Deng, Chang, et al.
Published: (2025)
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood
by: Aragam, Bryon, et al.
Published: (2024)
by: Aragam, Bryon, et al.
Published: (2024)
What is causal about causal models and representations?
by: Jørgensen, Frederik Hytting, et al.
Published: (2025)
by: Jørgensen, Frederik Hytting, et al.
Published: (2025)
Optimal structure learning and conditional independence testing
by: Gao, Ming, et al.
Published: (2025)
by: Gao, Ming, et al.
Published: (2025)
Markov Equivalence and Consistency in Differentiable Structure Learning
by: Deng, Chang, et al.
Published: (2024)
by: Deng, Chang, et al.
Published: (2024)
Learning general conditional independence structures via the neighbourhood lattice
by: Amini, Arash A., et al.
Published: (2022)
by: Amini, Arash A., et al.
Published: (2022)
Towards Interpretable Deep Generative Models via Causal Representation Learning
by: Moran, Gemma E., et al.
Published: (2025)
by: Moran, Gemma E., et al.
Published: (2025)
Optimality and computational barriers in variable selection under dependence
by: Gao, Ming, et al.
Published: (2025)
by: Gao, Ming, et al.
Published: (2025)
Optimal rates for density and mode estimation with expand-and-sparsify representations
by: Sinha, Kaushik, et al.
Published: (2026)
by: Sinha, Kaushik, et al.
Published: (2026)
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond
by: Chen, Fan, et al.
Published: (2022)
by: Chen, Fan, et al.
Published: (2022)
Provable Reward-Agnostic Preference-Based Reinforcement Learning
by: Zhan, Wenhao, et al.
Published: (2023)
by: Zhan, Wenhao, et al.
Published: (2023)
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm
by: Lim, Yooseok, et al.
Published: (2024)
by: Lim, Yooseok, et al.
Published: (2024)
Learning Hierarchical Polynomials of Multiple Nonlinear Features with Three-Layer Networks
by: Fu, Hengyu, et al.
Published: (2024)
by: Fu, Hengyu, et al.
Published: (2024)
Beyond Demand Estimation: Consumer Surplus Evaluation via Cumulative Propensity Weights
by: Bian, Zeyu, et al.
Published: (2026)
by: Bian, Zeyu, et al.
Published: (2026)
Neural Networks Learn Generic Multi-Index Models Near Information-Theoretic Limit
by: Zhang, Bohan, et al.
Published: (2025)
by: Zhang, Bohan, et al.
Published: (2025)
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
by: Chen, Tianyu, et al.
Published: (2023)
by: Chen, Tianyu, et al.
Published: (2023)
How Particle-System Random Batch Methods Enhance Graph Transformer: Memory Efficiency and Parallel Computing Strategy
by: Liu, Hanwen, et al.
Published: (2025)
by: Liu, Hanwen, et al.
Published: (2025)
Identifying General Mechanism Shifts in Linear Causal Representations
by: Chen, Tianyu, et al.
Published: (2024)
by: Chen, Tianyu, et al.
Published: (2024)
Compression, Generalization and Learning
by: Campi, Marco C., et al.
Published: (2023)
by: Campi, Marco C., et al.
Published: (2023)
Online Learning with Unknown Constraints
by: Sridharan, Karthik, et al.
Published: (2024)
by: Sridharan, Karthik, et al.
Published: (2024)
Adaptive Sample Aggregation In Transfer Learning
by: Hanneke, Steve, et al.
Published: (2024)
by: Hanneke, Steve, et al.
Published: (2024)
Conformal Prediction for Privacy-Preserving Machine Learning
by: Balinsky, Alexander David, et al.
Published: (2025)
by: Balinsky, Alexander David, et al.
Published: (2025)
Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)
by: Rodríguez-Vítores, David, et al.
Published: (2025)
Cost-optimal Sequential Testing via Doubly Robust Q-learning
by: Zhou, Doudou, et al.
Published: (2026)
by: Zhou, Doudou, et al.
Published: (2026)
Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Chemical Reaction Networks Learn Better than Spiking Neural Networks
by: Jaffard, Sophie, et al.
Published: (2026)
by: Jaffard, Sophie, et al.
Published: (2026)
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by: Foster, Dylan J., et al.
Published: (2024)
by: Foster, Dylan J., et al.
Published: (2024)
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
by: Hazard, Christopher J., et al.
Published: (2025)
by: Hazard, Christopher J., et al.
Published: (2025)
A Statistical Analysis of Deep Federated Learning for Intrinsically Low-dimensional Data
by: Chakraborty, Saptarshi, et al.
Published: (2024)
by: Chakraborty, Saptarshi, et al.
Published: (2024)
Scaling Laws in Linear Regression: Compute, Parameters, and Data
by: Lin, Licong, et al.
Published: (2024)
by: Lin, Licong, et al.
Published: (2024)
Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
by: Dong, Zihan, et al.
Published: (2026)
by: Dong, Zihan, et al.
Published: (2026)
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions
by: Javanmard, Adel, et al.
Published: (2024)
by: Javanmard, Adel, et al.
Published: (2024)
Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
by: Zhao, Qingyue, et al.
Published: (2025)
by: Zhao, Qingyue, et al.
Published: (2025)
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
by: Kaissis, Georgios, et al.
Published: (2024)
by: Kaissis, Georgios, et al.
Published: (2024)
Precise gradient descent training dynamics for finite-width multi-layer neural networks
by: Han, Qiyang, et al.
Published: (2025)
by: Han, Qiyang, et al.
Published: (2025)
Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression
by: Chen, Fan, et al.
Published: (2025)
by: Chen, Fan, et al.
Published: (2025)
Differentially Private Two-Stage Gradient Descent for Instrumental Variable Regression
by: Liang, Haodong, et al.
Published: (2025)
by: Liang, Haodong, et al.
Published: (2025)
Similar Items
-
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
by: Rajendran, Goutham, et al.
Published: (2024) -
Greedy equivalence search for nonparametric graphical models
by: Aragam, Bryon
Published: (2024) -
Differentiable Structure Learning and Causal Discovery for General Binary Data
by: Deng, Chang, et al.
Published: (2025) -
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood
by: Aragam, Bryon, et al.
Published: (2024) -
What is causal about causal models and representations?
by: Jørgensen, Frederik Hytting, et al.
Published: (2025)