Feature selection (FS) is of paramount importance in classification problems characterized by a large number of features, in particular in the presence of small ...
May 2, 2022 · A preference-based feature selection algorithm. Algorithm 1 summarizes the steps required to compute the op- timal structure s and the ...
May 2, 2022 · A preference-based feature selection algorithm. Algorithm 1 summarizes the steps required to compute the op- timal structure s⋆ and the ...
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify ...
We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback regarding the variables (of few samples) they evaluate ...
In this paper, we present a general framework (dubbed CBOB) for designing interactive MORL algorithms that involve human-in-the-loop interaction, enabling the ...
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Jan 27, 2019 · Active learning with feedback on features and instances. ... Human action recognition optimization based on evolutionary feature subset selection.
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Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately, prior work either ...
We propose a Bayesian optimization (BO) method for the preference-based MOO, optimizing x through an inter- active (human-in-the-loop based) estimation of the ...
Feb 24, 2024 · So far, we have presented four methods for batch selection in active preference-based reward learning: greedy, medoids, boundary medoids and ...