68 citations · 88 across the 4 of their papers we have counts for
4 papers · 1 filter
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
Matthew L. Olson, Shusen Liu, Rushil Anirudh +3
Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit tr…
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations
Tobias Huber, Maximilian Demmler, Silvan Mertes +2
Counterfactual explanations are a common tool to explain artificial intelligence models. For Reinforcement Learning (RL) agents, they answer "Why not?" or "What if?" questions by i…
Contrastive Identification of Covariate Shift in Image Data
Matthew L. Olson, Thuy-Vy Nguyen, Gaurav Dixit +3
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. Howe…
Counterfactual States for Atari Agents via Generative Deep Learning
Matthew L. Olson, Lawrence Neal, Fuxin Li +1
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past…