DiCE: Diverse Counterfactual Explanations for Machine Learning Classifiers

Our group works on the unified questions of model stability, fairness and explanation. We believe that there are fundamental connections between stability (generalization), fairness, and explainability of an ML model.

At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying all these methods for data-driven decision…

We work on various aspects of data-driven algorithms including fast machine learning algorithms that can learn from large scale and noisy data, learning from natural language and multilingual data, and applications of these algorithms to Web Search, Computational Advertisement, Recommendation…