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11/21/2022
Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions
The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a mean...
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11/07/2022
Towards learning to explain with concept bottleneck models: mitigating information leakage
Concept bottleneck models perform classification by first predicting whi...
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10/05/2022
Feature Importance for Time Series Data: Improving KernelSHAP
Feature importance techniques have enjoyed widespread attention in the e...
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05/15/2022
Reductive MDPs: A Perspective Beyond Temporal Horizons
Solving general Markov decision processes (MDPs) is a computationally ha...
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03/23/2022
Asynchronous Collaborative Learning Across Data Silos
Machine learning algorithms can perform well when trained on large datas...
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10/12/2020
SURF: Improving classifiers in production by learning from busy and noisy end users
Supervised learning classifiers inevitably make mistakes in production, ...
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04/27/2020
Some people aren't worth listening to: periodically retraining classifiers with feedback from a team of end users
Document classification is ubiquitous in a business setting, but often t...
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06/28/2019