2 code implementations • 23 Sep 2020 • Kalina Jasinska-Kobus, Marek Wydmuch, Krzysztof Dembczynski, Mikhail Kuznetsov, Robert Busa-Fekete
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
no code implementations • 1 Jun 2019 • Robert Busa-Fekete, Krzysztof Dembczynski, Alexander Golovnev, Kalina Jasinska, Mikhail Kuznetsov, Maxim Sviridenko, Chao Xu
First, we show that finding a tree with optimal training cost is NP-complete, nevertheless there are some tractable special cases with either perfect approximation or exact solution that can be obtained in linear time in terms of the number of labels $m$.
no code implementations • 29 May 2019 • Utkarsh Upadhyay, Robert Busa-Fekete, Wojciech Kotlowski, David Pal, Balazs Szorenyi
Web crawling is the problem of keeping a cache of webpages fresh, i. e., having the most recent copy available when a page is requested.
no code implementations • 30 Jul 2018 • Viktor Bengs, Robert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier
The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits.
no code implementations • ICML 2018 • Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete
We also introduce a generalization of the model, in which the constraints on pairwise preferences are relaxed, and for which maximum likelihood estimation can be carried out based on a variation of the generalized iterative scaling algorithm.
no code implementations • NeurIPS 2018 • Ashok Cutkosky, Robert Busa-Fekete
Scaling up the training process of these models is crucial, but the most popular algorithm, Stochastic Gradient Descent (SGD), is a serial method that is surprisingly hard to parallelize.
no code implementations • ICML 2017 • Robert Busa-Fekete, Balazs Szorenyi, Paul Weng, Shie Mannor
We study the multi-armed bandit (MAB) problem where the agent receives a vectorial feedback that encodes many possibly competing objectives to be optimized.