Past Program Activities

Explainable k-Means and k-Medians Clustering
Speaker: Michal Moshkovitz
Aug. 10, 2020
Program: Summer Cluster: Interpretable Machine Learning
Why Causal Interpretations Matter for Algorithmic Bias Mitigation: A Legal Perspective
Speaker: Alice Xiang (Partnership on AI)
Aug. 6, 2020
Program: Summer Cluster: Interpretable Machine Learning
A Critical Look At Some Common Trends In IML Research
Speaker: Shai Ben-David (University of Waterloo)
Aug. 3, 2020
Program: Summer Cluster: Interpretable Machine Learning
Identifying Explanatory Training Examples via Relative Influence
Speaker: Karolina Dziugaite (Element AI)
Aug. 3, 2020
Program: Summer Cluster: Interpretable Machine Learning
Discovering Compositional Representations With Causal Graphs and Sketch Drawings
Speaker: Richard Zemel
Jul. 30, 2020
Program: Summer Cluster: Interpretable Machine Learning
Disentangled Interpretations and How We Can Use Them
Speaker: Chandan Singh (UC Berkeley)
Jul. 27, 2020
Program: Summer Cluster: Interpretable Machine Learning
Verifiably Truthful Mechanisms
Speaker: Simina Branzei (Purdue University)
Jul. 23, 2020
Program: Summer Cluster: Interpretable Machine Learning
A Unifying Philosophical Theory of AI Explanations
Speaker: Atoosa Kasirzadeh (University of Toronto & Australian National University)
Jul. 6, 2020
Program: Summer Cluster: Interpretable Machine Learning
Learning from Simple Explanations
Speaker: Sanjoy Dasgupta (UCSD)
Jul. 2, 2020
Program: Summer Cluster: Interpretable Machine Learning
Kickoff Meeting
Jun. 29, 2020
Program: Summer Cluster: Interpretable Machine Learning

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