10 citations · 24 across the 9 of their papers we have counts for
6 papers · 1 filter
Interpretable Differencing of Machine Learning Models
Swagatam Haldar, Diptikalyan Saha, Dennis Wei +2
Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model…
On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach
Dennis Wei, Rahul Nair, Amit Dhurandhar +3
Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interp…
Boolean Decision Rules for Reinforcement Learning Policy Summarisation
James McCarthy, Rahul Nair, Elizabeth Daly +2
Explainability of Reinforcement Learning (RL) policies remains a challenging research problem, particularly when considering RL in a safety context. Understanding the decisions and…
Leveraging Explanations in Interactive Machine Learning: An Overview
Stefano Teso, Öznur Alkan, Wolfang Stammer +1
Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental mod…
FROTE: Feedback Rule-Driven Oversampling for Editing Models
Öznur Alkan, Dennis Wei, Massimiliano Mattetti +3
Machine learning models may involve decision boundaries that change over time due to updates to rules and regulations, such as in loan approvals or claims management. However, in s…
Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization
Paulito P. Palmes, Akihiro Kishimoto, Radu Marinescu +2
The pipeline optimization problem in machine learning requires simultaneous optimization of pipeline structures and parameter adaptation of their elements. Having an elegant way to…