Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards
Fuente:
arXiv
Saved in:
| Main Authors: | Liaw, Sarah, Plaut, Benjamin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Avoiding Catastrophe in Online Learning by Asking for Help
by: Plaut, Benjamin, et al.
Published: (2024)
by: Plaut, Benjamin, et al.
Published: (2024)
Safe Learning Under Irreversible Dynamics via Asking for Help
by: Plaut, Benjamin, et al.
Published: (2025)
by: Plaut, Benjamin, et al.
Published: (2025)
Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents
by: Iscan, Mehmet
Published: (2026)
by: Iscan, Mehmet
Published: (2026)
Learning When to Trust in Contextual Bandits
by: Ghasemi, Majid, et al.
Published: (2026)
by: Ghasemi, Majid, et al.
Published: (2026)
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
by: Lu, Xiaodong, et al.
Published: (2026)
by: Lu, Xiaodong, et al.
Published: (2026)
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models
by: Tayebati, Sina, et al.
Published: (2025)
by: Tayebati, Sina, et al.
Published: (2025)
Bounded-Abstention Pairwise Learning to Rank
by: Ferrara, Antonio, et al.
Published: (2025)
by: Ferrara, Antonio, et al.
Published: (2025)
Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A
by: Plaut, Benjamin, et al.
Published: (2024)
by: Plaut, Benjamin, et al.
Published: (2024)
Exploration Unbound
by: Arumugam, Dilip, et al.
Published: (2024)
by: Arumugam, Dilip, et al.
Published: (2024)
Getting By Goal Misgeneralization With a Little Help From a Mentor
by: Trinh, Tu, et al.
Published: (2024)
by: Trinh, Tu, et al.
Published: (2024)
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
by: Sun, Hao, et al.
Published: (2023)
by: Sun, Hao, et al.
Published: (2023)
Fusing Reward and Dueling Feedback in Stochastic Bandits
by: Wang, Xuchuang, et al.
Published: (2025)
by: Wang, Xuchuang, et al.
Published: (2025)
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks
by: Skalse, Joar, et al.
Published: (2024)
by: Skalse, Joar, et al.
Published: (2024)
When Is Diversity Rewarded in Cooperative Multi-Agent Learning?
by: Amir, Michael, et al.
Published: (2025)
by: Amir, Michael, et al.
Published: (2025)
Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
From Contextual Combinatorial Semi-Bandits to Bandit List Classification: Improved Sample Complexity with Sparse Rewards
by: Erez, Liad, et al.
Published: (2025)
by: Erez, Liad, et al.
Published: (2025)
Neural Active Learning Beyond Bandits
by: Ban, Yikun, et al.
Published: (2024)
by: Ban, Yikun, et al.
Published: (2024)
Rewarding Intellectual Humility Learning When Not To Answer In Large Language Models
by: Jha, Abha, et al.
Published: (2026)
by: Jha, Abha, et al.
Published: (2026)
DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning
by: Ma, Xiaoteng, et al.
Published: (2020)
by: Ma, Xiaoteng, et al.
Published: (2020)
Global Rewards in Restless Multi-Armed Bandits
by: Raman, Naveen, et al.
Published: (2024)
by: Raman, Naveen, et al.
Published: (2024)
To Steer or Not to Steer? Mechanistic Error Reduction with Abstention for Language Models
by: Hedström, Anna, et al.
Published: (2025)
by: Hedström, Anna, et al.
Published: (2025)
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
by: Mohamed, Faisal, et al.
Published: (2026)
by: Mohamed, Faisal, et al.
Published: (2026)
Risk-Sensitive Reinforcement Learning with Exponential Criteria
by: Noorani, Erfaun, et al.
Published: (2022)
by: Noorani, Erfaun, et al.
Published: (2022)
Cost-Sensitive Unbiased Risk Estimation for Multi-Class Positive-Unlabeled Learning
by: Zhang, Miao, et al.
Published: (2025)
by: Zhang, Miao, et al.
Published: (2025)
Provably Efficient Reinforcement Learning for Adversarial Restless Multi-Armed Bandits with Unknown Transitions and Bandit Feedback
by: Xiong, Guojun, et al.
Published: (2024)
by: Xiong, Guojun, et al.
Published: (2024)
Networked Restless Multi-Arm Bandits with Reinforcement Learning
by: Zhang, Hanmo, et al.
Published: (2025)
by: Zhang, Hanmo, et al.
Published: (2025)
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
by: Azran, Guy, et al.
Published: (2023)
by: Azran, Guy, et al.
Published: (2023)
Can We Really Learn One Representation to Optimize All Rewards?
by: Zheng, Chongyi, et al.
Published: (2026)
by: Zheng, Chongyi, et al.
Published: (2026)
SemiReward: A General Reward Model for Semi-supervised Learning
by: Li, Siyuan, et al.
Published: (2023)
by: Li, Siyuan, et al.
Published: (2023)
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior
by: Zhou, Zhiyuan, et al.
Published: (2022)
by: Zhou, Zhiyuan, et al.
Published: (2022)
Reinforcement Learning with Stochastic Reward Machines
by: Corazza, Jan, et al.
Published: (2025)
by: Corazza, Jan, et al.
Published: (2025)
Reinforcement Learning with Exogenous States and Rewards
by: Trimponias, George, et al.
Published: (2023)
by: Trimponias, George, et al.
Published: (2023)
Reinforcement Learning with Symbolic Reward Machines
by: Krug, Thomas, et al.
Published: (2026)
by: Krug, Thomas, et al.
Published: (2026)
Reward Learning From Preference With Ties
by: Liu, Jinsong, et al.
Published: (2024)
by: Liu, Jinsong, et al.
Published: (2024)
Offline Reinforcement Learning with Imputed Rewards
by: Romeo, Carlo, et al.
Published: (2024)
by: Romeo, Carlo, et al.
Published: (2024)
Mitigating LLM Hallucinations via Conformal Abstention
by: Yadkori, Yasin Abbasi, et al.
Published: (2024)
by: Yadkori, Yasin Abbasi, et al.
Published: (2024)
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
Learning to Attack: A Bandit Approach to Adversarial Context Poisoning
by: Telikani, Ray, et al.
Published: (2026)
by: Telikani, Ray, et al.
Published: (2026)
On-line Learning in Tree MDPs by Treating Policies as Bandit Arms
by: Shah, Anvay, et al.
Published: (2026)
by: Shah, Anvay, et al.
Published: (2026)
Beyond Numeric Rewards: In-Context Dueling Bandits with LLM Agents
by: Xia, Fanzeng, et al.
Published: (2024)
by: Xia, Fanzeng, et al.
Published: (2024)
Similar Items
-
Avoiding Catastrophe in Online Learning by Asking for Help
by: Plaut, Benjamin, et al.
Published: (2024) -
Safe Learning Under Irreversible Dynamics via Asking for Help
by: Plaut, Benjamin, et al.
Published: (2025) -
Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents
by: Iscan, Mehmet
Published: (2026) -
Learning When to Trust in Contextual Bandits
by: Ghasemi, Majid, et al.
Published: (2026) -
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
by: Lu, Xiaodong, et al.
Published: (2026)