31 citations · 78 across the 8 of their papers we have counts for
8 papers
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Akanksha Atrey, Kaleigh Clary, David Jensen
Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice in…
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Amanda Gentzel, Dan Garant, David Jensen
Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An ac…
Bayesian causal inference via probabilistic program synthesis
Sam Witty, Alexander Lew, David Jensen +1
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions.…
Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments
Kaleigh Clary, Emma Tosch, John Foley +1
Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itse…
ToyBox: Better Atari Environments for Testing Reinforcement Learning Agents
John Foley, Emma Tosch, Kaleigh Clary +1
It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agent…
Evaluating Causal Models by Comparing Interventional Distributions
Dan Garant, David Jensen
The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluat…