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Machine learning with a reject option: A survey
K Hendrickx, L Perini, D Van der Plas, W Meert… - Machine Learning, 2024 - Springer
Abstract Machine learning models always make a prediction, even when it is likely to be
inaccurate. This behavior should be avoided in many decision support applications, where …
inaccurate. This behavior should be avoided in many decision support applications, where …
Beyond perturbations: Learning guarantees with arbitrary adversarial test examples
We present a transductive learning algorithm that takes as input training examples from a
distribution P and arbitrary (unlabeled) test examples, possibly chosen by an adversary. This …
distribution P and arbitrary (unlabeled) test examples, possibly chosen by an adversary. This …
Learning to defer in content moderation: The human-ai interplay
Successful content moderation in online platforms relies on a human-AI collaboration
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …
approach. A typical heuristic estimates the expected harmfulness of a post and uses fixed …
Adversarial resilience in sequential prediction via abstention
We study the problem of sequential prediction in the stochastic setting with an adversary that
is allowed to inject clean-label adversarial (or out-of-distribution) examples. Algorithms …
is allowed to inject clean-label adversarial (or out-of-distribution) examples. Algorithms …
Online learning with sublinear best-action queries
In online learning, a decision maker repeatedly selects one of a set of actions, with the goal
of minimizing the overall loss incurred. Following the recent line of research on algorithms …
of minimizing the overall loss incurred. Following the recent line of research on algorithms …
Online learning with abstention
We present an extensive study of a key problem in online learning where the learner can opt
to abstain from making a prediction, at a certain cost. In the adversarial setting, we show how …
to abstain from making a prediction, at a certain cost. In the adversarial setting, we show how …
Partially interpretable models with guarantees on coverage and accuracy
Simple, sufficient explanations furnished by short decision lists can be useful for guiding
stakeholder actions. Unfortunately, this transparency can come at the expense of the higher …
stakeholder actions. Unfortunately, this transparency can come at the expense of the higher …
The extended littlestone's dimension for learning with mistakes and abstentions
This paper studies classification with an abstention option in the online setting. In this
setting, examples arrive sequentially, the learner is given a hypothesis class\mathcalH, and …
setting, examples arrive sequentially, the learner is given a hypothesis class\mathcalH, and …
Online decision mediation
Consider learning a decision support assistant to serve as an intermediary between (oracle)
expert behavior and (imperfect) human behavior: At each time, the algorithm observes an …
expert behavior and (imperfect) human behavior: At each time, the algorithm observes an …
Active online learning with hidden shifting domains
Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label
at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively …
at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively …