5 papers
Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning
Ha Manh Bui, Metod Jazbec, Eric Nalisnick +1
Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficient, O2O-RL is susceptible to…
Q-Learning with Shift-Aware Upper Confidence Bound in Non-Stationary Reinforcement Learning
Ha Manh Bui, Felix Parker, Kimia Ghobadi +1
We study the Non-Stationary Reinforcement Learning (RL) under distribution shifts in both finite-horizon episodic and infinite-horizon discounted Markov Decision Processes (MDPs).…
Calibrated Uncertainty Sampling for Active Learning
Ha Manh Bui, Iliana Maifeld-Carucci, Anqi Liu
We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is query…
Application of Multivariate Selective Bandwidth Kernel Density Estimation for Data Correction
Hai Bui, Mostafa Bakhoday-Paskyabi
This paper presents an intuitive application of multivariate kernel density estimation (KDE) for data correction. The method utilizes the expected value of the conditional probabil…
Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits
Ha Manh Bui, Enrique Mallada, Anqi Liu
By leveraging the representation power of deep neural networks, neural upper confidence bound (UCB) algorithms have shown success in contextual bandits. To further balance the expl…