40 citations · 53 across the 3 of their papers we have counts for
10 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…
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…
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
Michele Donini, Luca Franceschi, Massimiliano Pontil +2
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure…