Search Results for author:
Found 14 papers, 5 papers with code
Best Arm Identification for Prompt Learning under a Limited Budget
Based on this connection, a general framework TRIPLE (besT aRm Identification for Prompt LEarning) is proposed to harness the power of BAI-FB in prompt learning systematically.
Harnessing the Power of Federated Learning in Federated Contextual Bandits
Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original focus on supervised learning.
Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources
Then, a novel HetPEVI algorithm is proposed, which simultaneously considers the sample uncertainties from a finite number of data samples per data source and the source uncertainties due to a finite number of available data sources.
On High-dimensional and Low-rank Tensor Bandits
To address this limitation, this work studies a general tensor bandits model, where actions and system parameters are represented by tensors as opposed to vectors, and we particularly focus on the case that the unknown system tensor is low-rank.
Reward Teaching for Federated Multi-armed Bandits
Rigorous analyses demonstrate that when facing clients with UCB1, TWL outperforms TAL in terms of the dependencies on sub-optimality gaps thanks to its adaptive design.
A Self-Play Posterior Sampling Algorithm for Zero-Sum Markov Games
Existing studies on provably efficient algorithms for Markov games (MGs) almost exclusively build on the "optimism in the face of uncertainty" (OFU) principle.
Nearly Minimax Optimal Offline Reinforcement Learning with Linear Function Approximation: Single-Agent MDP and Markov Game
We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm.
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
In this paper, we propose BEACON -- Batched Exploration with Adaptive COmmunicatioN -- that closes this gap.
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
In this work, we break this barrier and study incentivized exploration with multiple and long-term strategic agents, who have more complicated behaviors that often appear in real-world applications.
Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions
We study a new stochastic multi-player multi-armed bandits (MP-MAB) problem, where the reward distribution changes if a collision occurs on the arm.
Federated Multi-armed Bandits with Personalization
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of FL with personalization.
Federated Multi-Armed Bandits
We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution.
On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications
Instead of focusing on the hardness of multiple players, we introduce a new dimension of hardness, called attackability.
Decentralized Multi-player Multi-armed Bandits with No Collision Information
The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper.
