108 citations · 178 across the 7 of their papers we have counts for
11 papers
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud
Michaela Hardt, Xiaoguang Chen, Xiaoyi Cheng +18
Understanding the predictions made by machine learning (ML) models and their potential biases remains a challenging and labor-intensive task that depends on the application, the da…
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar +1
As the complexity of machine learning (ML) models increases, resulting in a lack of prediction explainability, several methods have been developed to explain a model's behavior in…
Multi-objective Asynchronous Successive Halving
Robin Schmucker, Michele Donini, Muhammad Bilal Zafar +2
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-…
On the Lack of Robust Interpretability of Neural Text Classifiers
Muhammad Bilal Zafar, Michele Donini, Dylan Slack +3
With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models. One of the most well-adopted…
Loss-Aversively Fair Classification
Junaid Ali, Muhammad Bilal Zafar, Adish Singla +1
The use of algorithmic (learning-based) decision making in scenarios that affect human lives has motivated a number of recent studies to investigate such decision making systems fo…