most citedImproving Learning-to-Defer Algorithms Through Fine-Tuning

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cs.LG2023

Mahalanobis-Aware Training for Out-of-Distribution Detection

Connor Mclaughlin, Jason Matterer, Michael Yee

While deep learning models have seen widespread success in controlled environments, there are still barriers to their adoption in open-world settings. One critical task for safe de…

cs.LG2021★ 1 cited

Improving Learning-to-Defer Algorithms Through Fine-Tuning

Naveen Raman, Michael Yee

The ubiquity of AI leads to situations where humans and AI work together, creating the need for learning-to-defer algorithms that determine how to partition tasks between AI and hu…