1 citations · 3 across the 9 of their papers we have counts for
14 papers · 1 filter
Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning
Yash Jhaveri, Harley Wiltzer, Patrick Shafto +2
In the pursuit of finding an optimal policy, reinforcement learning (RL) methods generally ignore the properties of learned policies apart from their expected return. Thus, even wh…
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
Harley Wiltzer, Marc G. Bellemare, David Meger +2
When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In turn, their performance is inconsistent…
Evolution of beliefs in social networks
Pushpi Paranamana, Pei Wang, Patrick Shafto
Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have s…
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu, Patrick Shafto
It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success in adopti…
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Tomas Folke, Scott Cheng-Hsin Yang, Sean Anderson +1
Limited expert time is a key bottleneck in medical imaging. Due to advances in image classification, AI can now serve as decision-support for medical experts, with the potential fo…
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
Arash Givchi, Pei Wang, Junqi Wang +1
We consider constrained policy optimization in Reinforcement Learning, where the constraints are in form of marginals on state visitations and global action executions. Given these…