Zoom Room 1

• Bhatt et al. "Machine Learning Explainability for External Stakeholders"
• Karimi et al. "Algorithmic recourse under imperfect causal knowledge: a probabilistic approach"
• Wang et al. "Towards Probabilistic Sufficient Explanations"

[ protected link dropped ]

Zoom Room 2

• Agarwal et al. "Neural Additive Models: Interpretable Machine Learning with Neural Nets"
• Alaniz et al. "Learning Decision Trees Recurrently through Communication"
• Anders et al. "XAI for Analyzing and Unlearning Spurious Correlations in ImageNet"

[ protected link dropped ]

Zoom Room 3

• Dasgupta et al. "Explainable k-Means Clustering: Theory and Practice"
• Dhurandhar et al. "Leveraging Simple Model Predictions for Enhancing its Performance"
• Brophy and Lowd: "TREX: Tree-Ensemble Representer-Point Explanations"

[ protected link dropped ]

Zoom Room 4

• Lin et al. "Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions"
• Yau et al. "What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes"
• Danesh et al. "Understanding Finite-State Representations of Recurrent Policy Networks"

[ protected link dropped ]

Zoom Room 5

• Quint et al. "Contrastive Attribution with Feature Visualization"
• Zhao "Fast Real-time Counterfactual Explanations"
• Chrysos et al. "Unsupervised Controllable Generation with Self-Training"

[ protected link dropped ]