Interpretable Machine Learning Reading Group

This semester (2018 Fall), the Interpretable ML Reading Group meets every Tuesday at 1:00-2:00 pm, in CS 5331. Details will be announced via the mailing list. Below we maintain an archive of past presentations.

Date	Title	Speaker
10/16/2018	Towards Robust Interpretability with Self-Explaining Neural Networks	Xuezhou Zhang
05/10/2018	Probably Approximately Metric-Fair Learning	Yifeng Teng
04/13/2018	Learning Adversarially Fair and Transferable Representations	Xuezhou Zhang
04/06/2018	Differentiable Abstract Interpretation for Provably Robust Neural Networks	Samuel Drews
03/16/2018	Online Learning with an Unknown Fairness Metric	Xuezhou Zhang
03/02/2018	Fair Clustering Through Fairlets	Yifeng Teng
02/22/2018	Detecting Bias in Black-Box Models Using Transparent Model Distillation	Xuezhou Zhang
11/15/2017	Interpreting Classifiers through Attribute Interactions in Datasets	Samuel Drews
11/01/2017	A Convex Framework for Fair Regression	Yifeng Teng
10/25/2017	On Fairness and Calibration	David Merrell
10/18/2017	Decoupled classifiers for fair and efficient machine learning	Xuezhou Zhang
10/11/2017	Counterfactual Fairness	Group Discussion
10/04/2017	Avoiding Discrimination through Causal Reasoning	Group Discussion
09/27/2017	Algorithmic Transparency via Quantitative Input Influence	Samuel Drews
09/20/2017	Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings	Yingyu Liang
08/23/2017	Proxy Discrimination in Data-Driven Systems	Aws Albarghouthi
08/16/2017	Algorithmic decision making and the cost of fairness	Yifeng Teng
08/09/2017	Fairness in Reinforcement Learning	David Merrell
08/02/2017	Equality of Opportunity in Supervised Learning	Xuezhou Zhang