Amit Dhurandhar

Learning with Changing Features

2017 · arXiv (Cornell University)

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. In particular, we propose an approach to provably …

ProtoDash: Fast Interpretable Prototype Selection

2017 · arXiv (Cornell University)

In this paper we propose an efficient algorithm ProtoDash for selecting prototypical examples from complex datasets. Our work builds on top of the learn to criticize (L2C) work by Kim et al. (2016) and generalizes …

Learning Global Transparent Models from Local Contrastive Explanations

2020 · arXiv (Cornell University)

There is a rich and growing literature on producing local point wise contrastive/counterfactual explanations for complex models. These methods highlight what is important to justify the classification and/or produce a contrast point that alters the …

Local Explanations for Reinforcement Learning

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

Many works in explainable AI have focused on explaining black-box classification models. Explaining deep reinforcement learning (RL) policies in a manner that could be understood by domain users has received much less attention. In this …