RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits

arXiv:2604.00531 · cs.LG · Submitted 2026-04-01 · Read on arXiv

cs.LG

Submitted: 2026-04-01

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 29× in this paper · explained in 3 episodes
• Representation Learning — 13× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Trace — 5× in this paper · explained in 2 episodes
• Contextual Bandits — 3× in this paper · explained in 3 episodes
• Meta-learning — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Sample Complexity — 2× in this paper · explained in 15 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Upper Confidence Bound — 2× in this paper
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alternating Minimization — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 14 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Matrix Completion — 1× in this paper · explained in 3 episodes
• Optimism in the Face of Uncertainty Linear — 1× in this paper
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• Sample Efficient Linear Meta-Learning by Alternating Minimization
• LoRe: Personalizing LLMs via Low-Rank Reward Modeling
• Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks
• Impact of Representation Learning in Linear Bandits
• Noisy Low Rank Column-wise Sensing
• Sequential Batch Learning in Finite-Action Linear Contextual Bandits

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