Preference Learning

By Johannes Fürnkranz, Eyke Hüllermeier. 2010

315 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

A Survey of Preference-Based Reinforcement Learning Methods (2017, background): “Depending on what constitutes the set of choices Z, Fürnkranz and Hüllermeier (2010) distinguish between learning from object preferences, where the training data is given in the form of pairwise comparisons between data objects, and learning from label preferences, where the training data is given…”
Collaborative Gaussian Processes for Preference Learning (2012, background): “The same kernel function can be derived from a large margin classification viewpoint [6].”
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (2020, background): “, 2018), including content search (Fürnkranz and Hüllermeier, 2010), image retrieval (Wah et al.”
Preference-based Online Learning with Dueling Bandits: A Survey (2018, background): “More generally, the lack of precise feedback or exact supervision has been observed in other branches of machine learning, too, and has led to the emergence of ﬁelds such as weakly supervised learning and preference learning (F¨urnkranz and H¨ullermeier, 2011).”
Copeland Dueling Bandits (2015, background): “As with other preference learning problems [5], feedback consists of a pairwise preference between a selected pair of arms, instead of scalar reward for a single selected arm, as in the K-armed bandit problem.”
Preference-based Evolutionary Direct Policy Search (2013, background): “Therefore, the authors propose an alternative formalization, in which histories are compared in a qualitative way: h′ h if the patient survives in h but not in h′, and both histories are incomparable (h′⊥h) if the patient does neither survive in h′ nor in h.”