5 papers · 1 filter
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…
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…
Density-Softmax: Efficient Test-time Model for Uncertainty Estimation and Robustness under Distribution Shifts
Ha Manh Bui, Anqi Liu
Sampling-based methods, e.g., Deep Ensembles and Bayesian Neural Nets have become promising approaches to improve the quality of uncertainty estimation and robust generalization. H…