Two-stage learning to defer with multiple experts
We study a two-stage scenario for learning to defer with multiple experts, which is crucial in
practice for many applications. In this scenario, a predictor is derived in a first stage by …
practice for many applications. In this scenario, a predictor is derived in a first stage by …
Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens
Major depressive disorder is a debilitating disease affecting 264 million people worldwide.
While many antidepressant medications are available, few clinical guidelines support …
While many antidepressant medications are available, few clinical guidelines support …
Who should predict? exact algorithms for learning to defer to humans
Automated AI classifiers should be able to defer the prediction to a human decision maker to
ensure more accurate predictions. In this work, we jointly train a classifier with a rejector …
ensure more accurate predictions. In this work, we jointly train a classifier with a rejector …
Sample efficient learning of predictors that complement humans
One of the goals of learning algorithms is to complement and reduce the burden on human
decision makers. The expert deferral setting wherein an algorithm can either predict on its …
decision makers. The expert deferral setting wherein an algorithm can either predict on its …
Principled approaches for learning to defer with multiple experts
We present a study of surrogate losses and algorithms for the general problem of learning to
defer with multiple experts. We first introduce a new family of surrogate losses specifically …
defer with multiple experts. We first introduce a new family of surrogate losses specifically …
Learning to defer to multiple experts: Consistent surrogate losses, confidence calibration, and conformal ensembles
We study the statistical properties of learning to defer (L2D) to multiple experts. In particular,
we address the open problems of deriving a consistent surrogate loss, confidence …
we address the open problems of deriving a consistent surrogate loss, confidence …
Learning to Defer to a Population: A Meta-Learning Approach
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by
allocating difficult decisions to a human expert. All existing work on L2D assumes that each …
allocating difficult decisions to a human expert. All existing work on L2D assumes that each …
[PDF][PDF] Pre-emptive learning-to-defer for sequential medical decision-making under uncertainty
Abstract We propose SLTD ('Sequential Learning-to-Defer') a framework for learning-to-
defer pre-emptively to an expert in sequential decision-making settings. SLTD measures the …
defer pre-emptively to an expert in sequential decision-making settings. SLTD measures the …
Human–AI collaborative multi-modal multi-rater learning for endometriosis diagnosis
Objective. Endometriosis, affecting about 10% of individuals assigned female at birth, is
challenging to diagnose and manage. Diagnosis typically involves the identification of …
challenging to diagnose and manage. Diagnosis typically involves the identification of …
Realizable -Consistent and Bayes-Consistent Loss Functions for Learning to Defer
We present a comprehensive study of surrogate loss functions for learning to defer. We
introduce a broad family of surrogate losses, parameterized by a non-increasing function …
introduce a broad family of surrogate losses, parameterized by a non-increasing function …