Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
Fuente:
arXiv
Saved in:
| Main Authors: | Goel, Surbhi, Pei, Jonathan, Wang, James |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
by: Goel, Surbhi, et al.
Published: (2024)
by: Goel, Surbhi, et al.
Published: (2024)
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
by: Wang, James, et al.
Published: (2026)
by: Wang, James, et al.
Published: (2026)
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
by: Qiu, GuanWen, et al.
Published: (2024)
by: Qiu, GuanWen, et al.
Published: (2024)
Causal Imitation Learning Under Measurement Error and Distribution Shift
by: Bo, Shi, et al.
Published: (2026)
by: Bo, Shi, et al.
Published: (2026)
Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds
by: Edelman, Ezra, et al.
Published: (2026)
by: Edelman, Ezra, et al.
Published: (2026)
Offline Imitation Learning upon Arbitrary Demonstrations by Pre-Training Dynamics Representations
by: Ma, Haitong, et al.
Published: (2025)
by: Ma, Haitong, et al.
Published: (2025)
When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
by: Agrawal, Rishabh, et al.
Published: (2026)
by: Agrawal, Rishabh, et al.
Published: (2026)
Testing Noise Assumptions of Learning Algorithms
by: Goel, Surbhi, et al.
Published: (2025)
by: Goel, Surbhi, et al.
Published: (2025)
The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
by: Edelman, Benjamin L., et al.
Published: (2024)
by: Edelman, Benjamin L., et al.
Published: (2024)
Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
by: Ning, Alex, et al.
Published: (2025)
by: Ning, Alex, et al.
Published: (2025)
Is Your Imitation Learning Policy Better than Mine? Policy Comparison with Near-Optimal Stopping
by: Snyder, David, et al.
Published: (2025)
by: Snyder, David, et al.
Published: (2025)
In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
by: Panigrahi, Abhishek, et al.
Published: (2025)
by: Panigrahi, Abhishek, et al.
Published: (2025)
Lessons Learned: Reproducibility, Replicability, and When to Stop
by: Gomez, Milton S., et al.
Published: (2024)
by: Gomez, Milton S., et al.
Published: (2024)
Generalization Bounds and Stopping Rules for Learning with Self-Selected Data
by: Rodemann, Julian, et al.
Published: (2025)
by: Rodemann, Julian, et al.
Published: (2025)
Interpretable Imitation Learning with Dynamic Causal Relations
by: Zhao, Tianxiang, et al.
Published: (2023)
by: Zhao, Tianxiang, et al.
Published: (2023)
When to Stop Federated Learning: Zero-Shot Generation of Synthetic Validation Data with Generative AI for Early Stopping
by: Lee, Youngjoon, et al.
Published: (2025)
by: Lee, Youngjoon, et al.
Published: (2025)
Transfer Learning for Meta-analysis Under Covariate Shift
by: Wang, Zilong, et al.
Published: (2026)
by: Wang, Zilong, et al.
Published: (2026)
Why Do Transformers Fail to Forecast Time Series In-Context?
by: Zhou, Yufa, et al.
Published: (2025)
by: Zhou, Yufa, et al.
Published: (2025)
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks
by: Sabbaghi, Mahdi, et al.
Published: (2024)
by: Sabbaghi, Mahdi, et al.
Published: (2024)
FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
by: Fenoglio, Dario, et al.
Published: (2025)
by: Fenoglio, Dario, et al.
Published: (2025)
Vision Transformers that Never Stop Learning
by: Sun, Caihao, et al.
Published: (2026)
by: Sun, Caihao, et al.
Published: (2026)
Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)
by: Magnino, Lorenzo, et al.
Published: (2024)
Continual Learning Under Language Shift
by: Gogoulou, Evangelia, et al.
Published: (2023)
by: Gogoulou, Evangelia, et al.
Published: (2023)
Learning Clinical Representations Under Systematic Distribution Shift
by: Zhang, Yuanyun, et al.
Published: (2026)
by: Zhang, Yuanyun, et al.
Published: (2026)
Adversarial Imitation Learning via Boosting
by: Chang, Jonathan D., et al.
Published: (2024)
by: Chang, Jonathan D., et al.
Published: (2024)
Progressive distillation induces an implicit curriculum
by: Panigrahi, Abhishek, et al.
Published: (2024)
by: Panigrahi, Abhishek, et al.
Published: (2024)
DataMIL: Selecting Data for Robot Imitation Learning with Datamodels
by: Dass, Shivin, et al.
Published: (2025)
by: Dass, Shivin, et al.
Published: (2025)
Stochastic Bandits with ReLU Neural Networks
by: Xu, Kan, et al.
Published: (2024)
by: Xu, Kan, et al.
Published: (2024)
Adversarial Resilience in Sequential Prediction via Abstention
by: Goel, Surbhi, et al.
Published: (2023)
by: Goel, Surbhi, et al.
Published: (2023)
A Theory of Learning with Autoregressive Chain of Thought
by: Joshi, Nirmit, et al.
Published: (2025)
by: Joshi, Nirmit, et al.
Published: (2025)
When to Stop Reusing: Dynamic Gradient Gating for Sample-Efficient RLVR
by: Miao, Yuchun, et al.
Published: (2026)
by: Miao, Yuchun, et al.
Published: (2026)
Continual Learning as Shared-Manifold Continuation Under Compatible Shift
by: Kobs, Henry J.
Published: (2026)
by: Kobs, Henry J.
Published: (2026)
Latent Wasserstein Adversarial Imitation Learning
by: Yang, Siqi, et al.
Published: (2026)
by: Yang, Siqi, et al.
Published: (2026)
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
by: Dharmakeerthi, Kulunu, et al.
Published: (2024)
by: Dharmakeerthi, Kulunu, et al.
Published: (2024)
Imitation Learning from Purified Demonstrations
by: Wang, Yunke, et al.
Published: (2023)
by: Wang, Yunke, et al.
Published: (2023)
Dynamic Angle Selection in X-Ray CT: A Reinforcement Learning Approach to Optimal Stopping
by: Wang, Tianyuan, et al.
Published: (2025)
by: Wang, Tianyuan, et al.
Published: (2025)
Early Stopping Tabular In-Context Learning
by: Küken, Jaris, et al.
Published: (2025)
by: Küken, Jaris, et al.
Published: (2025)
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)
by: Tian, Chunlin, et al.
Published: (2024)
A Numerical Study of Chaotic Dynamics of K-S Equation with FNOs
by: Khetrapal, Surbhi, et al.
Published: (2024)
by: Khetrapal, Surbhi, et al.
Published: (2024)
On the Sample Efficiency of Inverse Dynamics Models for Semi-Supervised Imitation Learning
by: Morin, Sacha, et al.
Published: (2026)
by: Morin, Sacha, et al.
Published: (2026)
Similar Items
-
Tolerant Algorithms for Learning with Arbitrary Covariate Shift
by: Goel, Surbhi, et al.
Published: (2024) -
Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
by: Wang, James, et al.
Published: (2026) -
Complexity Matters: Dynamics of Feature Learning in the Presence of Spurious Correlations
by: Qiu, GuanWen, et al.
Published: (2024) -
Causal Imitation Learning Under Measurement Error and Distribution Shift
by: Bo, Shi, et al.
Published: (2026) -
Reliable Abstention under Adversarial Injections: Tight Lower Bounds and New Upper Bounds
by: Edelman, Ezra, et al.
Published: (2026)