research
∙
01/03/2023
Contextual Conservative Q-Learning for Offline Reinforcement Learning
Offline reinforcement learning learns an effective policy on offline dat...
share
research
∙
11/28/2022
Deep Semi-supervised Learning with Double-Contrast of Features and Semantics
In recent years, the field of intelligent transportation systems (ITS) h...
research
∙
11/16/2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Comparing Bayesian neural networks (BNNs) with different widths is chall...
research
∙
08/02/2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
For responsible decision making in safety-critical settings, machine lea...
research
∙
07/13/2022
Policy Optimization with Sparse Global Contrastive Explanations
We develop a Reinforcement Learning (RL) framework for improving an exis...
research
∙
04/11/2022
Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts
Self-supervised learning (SSL), as a newly emerging unsupervised represe...
research
∙
04/13/2020
Power-Constrained Bandits
Contextual bandits often provide simple and effective personalization in...
research
∙
06/24/2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Bayesian Neural Networks (BNNs) place priors over the parameters in a ne...
research
∙
05/15/2019
Output-Constrained Bayesian Neural Networks
Bayesian neural network (BNN) priors are defined in parameter space, mak...
research
∙
11/16/2018
Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
While modern neural networks are making remarkable gains in terms of pre...
research
∙
06/13/2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Bayesian Neural Networks (BNNs) have recently received increasing attent...
research
∙
05/31/2018
