Cover Image

Semi-Supervised Learning under General Causal Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Moore, Archer, Shim, Heejung, Zhu, Jingge, Gong, Mingming
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models
by: Wu, Xuetong, et al.
Published: (2022)

Semi-Supervised Learning for Deep Causal Generative Models
by: Ibrahim, Yasin, et al.
Published: (2024)

Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
by: Yoon, Heegeon, et al.
Published: (2026)

A DPI-PAC-Bayesian Framework for Generalization Bounds
by: Guan, Muhan, et al.
Published: (2025)

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
by: Cheng, Ziheng, et al.
Published: (2026)

On the Generalization for Transfer Learning: An Information-Theoretic Analysis
by: Wu, Xuetong, et al.
Published: (2022)

On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
by: Fan, Shunxing, et al.
Published: (2024)

A Semiparametric Approach to Causal Inference
by: Zhang, Archer Gong, et al.
Published: (2024)

Block-Sample MAC-Bayes Generalization Bounds
by: Frey, Matthias, et al.
Published: (2026)

Identifying Weight-Variant Latent Causal Models
by: Liu, Yuhang, et al.
Published: (2022)

In-Context Semi-Supervised Learning
by: Fan, Jiashuo, et al.
Published: (2025)

Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach
by: Zuo, Aoqi, et al.
Published: (2024)

Identifiable Latent Polynomial Causal Models Through the Lens of Change
by: Liu, Yuhang, et al.
Published: (2023)

Model-Change Active Learning in Graph-Based Semi-Supervised Learning
by: Miller, Kevin, et al.
Published: (2021)

Semi-Supervised Generative Learning via Latent Space Distribution Matching
by: Chong, Kwong Yu, et al.
Published: (2026)

Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
by: Seong, Hyunki, et al.
Published: (2024)

Fast Rate Information-theoretic Bounds on Generalization Errors
by: Wu, Xuetong, et al.
Published: (2023)

CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
by: Wu, Chengfeng, et al.
Published: (2026)

A PAC-Bayesian Analysis of Channel-Induced Degradation in Edge Inference
by: He, Yangshuo, et al.
Published: (2026)

Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation
by: Liang, Jiachen, et al.
Published: (2024)

Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
by: Gan, Kai, et al.
Published: (2024)

On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
by: Morin, Sacha, et al.
Published: (2026)

Semi-Supervised Contrastive Learning with Orthonormal Prototypes
by: Li, Huanran, et al.
Published: (2025)

Semi-Supervised Laplace Learning on Stiefel Manifolds
by: Holtz, Chester, et al.
Published: (2023)

Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
by: Liu, Yuhang, et al.
Published: (2024)

On the Generalization and Causal Explanation in Self-Supervised Learning
by: Qiang, Wenwen, et al.
Published: (2024)

Rethinking State Disentanglement in Causal Reinforcement Learning
by: Cao, Haiyao, et al.
Published: (2024)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
by: Lee, Se Yoon, et al.
Published: (2026)

Analysis of Semi-Supervised Learning on Hypergraphs
by: Weihs, Adrien, et al.
Published: (2025)

Reinforcement Learning-Guided Semi-Supervised Learning
by: Heidari, Marzi, et al.
Published: (2024)

Semi-Supervised Cross-Domain Imitation Learning
by: Chu, Li-Min, et al.
Published: (2026)

Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
by: Kutt, Brody, et al.
Published: (2022)

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
by: Audiffren, Julien, et al.
Published: (2026)

JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
by: Aghababaei-Harandi, Ali, et al.
Published: (2026)

Semi-Supervised Learning with Balanced Deep Representation Distributions
by: Li, Changchun, et al.
Published: (2026)

Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning
by: Chen, Shaohan, et al.
Published: (2024)

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited
by: Wang, Zheng, et al.
Published: (2023)

Spatio-Temporal Forecasting of Retaining Wall Deformation: Mitigating Error Accumulation via Multi-Resolution ConvLSTM Stacking Ensemble
by: Kim, Jihoon, et al.
Published: (2026)

Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
by: Trottet, Cécile, et al.
Published: (2023)