Cover Image

Test Time Training for Supervised Causal Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Deng, Zizhen, Zhang, Jiaru, Ding, Rui, Bojun, Huang, Wang, Jinzhuo, Fu, Qiang, Han, Shi, Zhang, Dongmei
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
by: Zhang, Jiaru, et al.
Published: (2025)

Test-Time Learning of Causal Structure from Interventional Data
by: Chen, Wei, et al.
Published: (2026)

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
by: Ma, Pingchuan, et al.
Published: (2024)

Deep Causal Learning: Representation, Discovery and Inference
by: Deng, Zizhen, et al.
Published: (2022)

PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
by: Zou, Jiaru, et al.
Published: (2024)

Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision
by: Xi, Zhiheng, et al.
Published: (2024)

Open-World Test-Time Training: Self-Training with Contrast Learning
by: Su, Houcheng, et al.
Published: (2024)

Test-Time Training on Graphs with Large Language Models (LLMs)
by: Zhang, Jiaxin, et al.
Published: (2024)

In-Place Test-Time Training
by: Feng, Guhao, et al.
Published: (2026)

LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data
by: Zhang, Jiajun, et al.
Published: (2024)

Benign Overfitting in Adversarial Training for Vision Transformers
by: Zhang, Jiaming, et al.
Published: (2026)

FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
by: Yan, Peishen, et al.
Published: (2026)

Absorber LLM: Harnessing Causal Synchronization for Test-Time Training
by: Zhang, Zhixin, et al.
Published: (2026)

TNT: Improving Chunkwise Training for Test-Time Memorization
by: Li, Zeman, et al.
Published: (2025)

Denoising-Aware Contrastive Learning for Noisy Time Series
by: Zhou, Shuang, et al.
Published: (2024)

Mixture of Experts based Multi-task Supervise Learning from Crowds
by: Han, Tao, et al.
Published: (2024)

Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
by: Chen, Hong, et al.
Published: (2026)

Self-Harmony: Learning to Harmonize Self-Supervision and Self-Play in Test-Time Reinforcement Learning
by: Wang, Ru, et al.
Published: (2025)

Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
by: Chang, Ching, et al.
Published: (2024)

Caracal: Causal Architecture via Spectral Mixing
by: Gan, Bingzheng, et al.
Published: (2026)

Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding
by: Zhang, Jiaru, et al.
Published: (2025)

Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models
by: Wu, Xiaoyu, et al.
Published: (2024)

REE-TTT: Highly Adaptive Radar Echo Extrapolation Based on Test-Time Training
by: Di, Xin, et al.
Published: (2026)

On the Universality of Self-Supervised Learning
by: Qiang, Wenwen, et al.
Published: (2024)

The Surprising Effectiveness of Test-Time Training for Few-Shot Learning
by: Akyürek, Ekin, et al.
Published: (2024)

Training and Evaluating Causal Forecasting Models for Time-Series
by: Crasson, Thomas, et al.
Published: (2024)

Meta-TTRL: A Metacognitive Framework for Self-Improving Test-Time Reinforcement Learning in Unified Multimodal Models
by: Tan, Lit Sin, et al.
Published: (2026)

TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling
by: Sun, Yifei, et al.
Published: (2025)

One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
by: Zhang, Haozhen, et al.
Published: (2024)

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
by: Lin, Haohong, et al.
Published: (2024)

Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
by: Zhang, Hengyuan, et al.
Published: (2024)

Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
by: Kim, Wonah, et al.
Published: (2025)

Step-by-Step Causality: Transparent Causal Discovery with Multi-Agent Tree-Query and Adversarial Confidence Estimation
by: Ding, Ziyi, et al.
Published: (2026)

When Normality Shifts: Risk-Aware Test-Time Adaptation for Unsupervised Tabular Anomaly Detection
by: Huang, Wei, et al.
Published: (2026)

Test-Time Training Undermines Safety Guardrails
by: Antonelli, Simone, et al.
Published: (2026)

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
by: Zhu, Xiao, et al.
Published: (2026)

Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
by: Zhang, Jiaxin, et al.
Published: (2026)

Adaptive Guidance for Local Training in Heterogeneous Federated Learning
by: Zhang, Jianqing, et al.
Published: (2024)

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy
by: Cai, Ruichu, et al.
Published: (2024)

SWE-Replay: Efficient Test-Time Scaling for Software Engineering Agents
by: Ding, Yifeng, et al.
Published: (2026)