Daniel Neider

Diffusion-generated time series are harder to detect than images, as a simple classifier beats a reconstruction-based method that works well for images.

Current counterfactual explanation methods fall apart when faced with the real-world messiness of missing data, even the "robust" ones.

Automating reward function design with Symbolic Reward Machines unlocks reinforcement learning for complex tasks without manual environment labeling.