Leveraging the Power of Conversations: Optimal Key Term Selection in Conversational Contextual Bandits
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Maoli, Li, Zhuohua, Dai, Xiangxiang, Lui, John C. S. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024)
by: Li, Zhuohua, et al.
Published: (2024)
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
by: Li, Zhuohua, et al.
Published: (2025)
by: Li, Zhuohua, et al.
Published: (2025)
A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses
by: Dai, Xiangxiang, et al.
Published: (2025)
by: Dai, Xiangxiang, et al.
Published: (2025)
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
by: Liu, Xutong, et al.
Published: (2023)
by: Liu, Xutong, et al.
Published: (2023)
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
by: Yang, Hantao, et al.
Published: (2024)
by: Yang, Hantao, et al.
Published: (2024)
Cost-Effective Online Multi-LLM Selection with Versatile Reward Models
by: Dai, Xiangxiang, et al.
Published: (2024)
by: Dai, Xiangxiang, et al.
Published: (2024)
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
by: Zhang, Zeyu, et al.
Published: (2026)
by: Zhang, Zeyu, et al.
Published: (2026)
Leveraging Offline Data in Linear Latent Contextual Bandits
by: Kausik, Chinmaya, et al.
Published: (2024)
by: Kausik, Chinmaya, et al.
Published: (2024)
Online Clustering of Dueling Bandits
by: Wang, Zhiyong, et al.
Published: (2025)
by: Wang, Zhiyong, et al.
Published: (2025)
Large Language Model-Enhanced Multi-Armed Bandits
by: Sun, Jiahang, et al.
Published: (2025)
by: Sun, Jiahang, et al.
Published: (2025)
HiLoRA: Adaptive Hierarchical LoRA Routing for Training-Free Domain Generalization
by: Han, Ziyi, et al.
Published: (2025)
by: Han, Ziyi, et al.
Published: (2025)
Fusing Reward and Dueling Feedback in Stochastic Bandits
by: Wang, Xuchuang, et al.
Published: (2025)
by: Wang, Xuchuang, et al.
Published: (2025)
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
by: Wang, Zhiyong, et al.
Published: (2024)
by: Wang, Zhiyong, et al.
Published: (2024)
Tree Ensembles for Contextual Bandits
by: Nilsson, Hannes, et al.
Published: (2024)
by: Nilsson, Hannes, et al.
Published: (2024)
A Contextual Combinatorial Bandit Approach to Negotiation
by: Li, Yexin, et al.
Published: (2024)
by: Li, Yexin, et al.
Published: (2024)
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Causal Contextual Bandits with Adaptive Context
by: Madhavan, Rahul, et al.
Published: (2024)
by: Madhavan, Rahul, et al.
Published: (2024)
Diffusion Models Meet Contextual Bandits
by: Aouali, Imad
Published: (2024)
by: Aouali, Imad
Published: (2024)
Learning When to Trust in Contextual Bandits
by: Ghasemi, Majid, et al.
Published: (2026)
by: Ghasemi, Majid, et al.
Published: (2026)
Combinatorial Logistic Bandits
by: Liu, Xutong, et al.
Published: (2024)
by: Liu, Xutong, et al.
Published: (2024)
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
by: Liu, Xutong, et al.
Published: (2022)
by: Liu, Xutong, et al.
Published: (2022)
The Sample Complexity of Multiclass and Sparse Contextual Bandits
by: Erez, Liad, et al.
Published: (2026)
by: Erez, Liad, et al.
Published: (2026)
Conservative Contextual Bandits: Beyond Linear Representations
by: Deb, Rohan, et al.
Published: (2024)
by: Deb, Rohan, et al.
Published: (2024)
Linear Contextual Bandits with Hybrid Payoff: Revisited
by: Das, Nirjhar, et al.
Published: (2024)
by: Das, Nirjhar, et al.
Published: (2024)
Variance-Dependent Regret Lower Bounds for Contextual Bandits
by: He, Jiafan, et al.
Published: (2025)
by: He, Jiafan, et al.
Published: (2025)
Second Order Bounds for Contextual Bandits with Function Approximation
by: Pacchiano, Aldo
Published: (2024)
by: Pacchiano, Aldo
Published: (2024)
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
by: Lu, Xiaodong, et al.
Published: (2026)
by: Lu, Xiaodong, et al.
Published: (2026)
Selective Prompting Tuning for Personalized Conversations with LLMs
by: Huang, Qiushi, et al.
Published: (2024)
by: Huang, Qiushi, et al.
Published: (2024)
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)
Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits
by: Sun, Jiazheng, et al.
Published: (2025)
by: Sun, Jiazheng, et al.
Published: (2025)
COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents
by: Verma, Arun, et al.
Published: (2025)
by: Verma, Arun, et al.
Published: (2025)
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
by: Shimizu, Tatsuhiro, et al.
Published: (2024)
Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits
by: Pershin, Maksim, et al.
Published: (2026)
by: Pershin, Maksim, et al.
Published: (2026)
Contextual Active Model Selection
by: Liu, Xuefeng, et al.
Published: (2022)
by: Liu, Xuefeng, et al.
Published: (2022)
Online Multi-LLM Selection via Contextual Bandits under Unstructured Context Evolution
by: Poon, Manhin, et al.
Published: (2025)
by: Poon, Manhin, et al.
Published: (2025)
Context Retrieval via Normalized Contextual Latent Interaction for Conversational Agent
by: Liu, Junfeng, et al.
Published: (2023)
by: Liu, Junfeng, et al.
Published: (2023)
Evaluating Very Long-Term Conversational Memory of LLM Agents
by: Maharana, Adyasha, et al.
Published: (2024)
by: Maharana, Adyasha, et al.
Published: (2024)
Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles
by: Kim, Jung-hun, et al.
Published: (2017)
by: Kim, Jung-hun, et al.
Published: (2017)
Impatient Bandits: Optimizing for the Long-Term Without Delay
by: Zhang, Kelly W., et al.
Published: (2025)
by: Zhang, Kelly W., et al.
Published: (2025)
Bi-Level Contextual Bandits for Individualized Resource Allocation under Delayed Feedback
by: Almasi, Mohammadsina, et al.
Published: (2025)
by: Almasi, Mohammadsina, et al.
Published: (2025)
Similar Items
-
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024) -
Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts
by: Li, Zhuohua, et al.
Published: (2025) -
A Multi-Agent Conversational Bandit Approach to Online Evaluation and Selection of User-Aligned LLM Responses
by: Dai, Xiangxiang, et al.
Published: (2025) -
Contextual Combinatorial Bandits with Probabilistically Triggered Arms
by: Liu, Xutong, et al.
Published: (2023) -
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
by: Yang, Hantao, et al.
Published: (2024)