no code implementations • 15 Oct 2022 • Alexander Buchholz, Vito Bellini, Giuseppe Di Benedetto, Yannik Stein, Matteo Ruffini, Fabian Moerchen
We suggest an approach to measure and disentangle the effect of simultaneous experiments by providing a cost sharing approach based on Shapley values.
no code implementations • 15 Oct 2022 • Alexander Buchholz, Ben London, Giuseppe Di Benedetto, Thorsten Joachims
A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production.
no code implementations • 12 May 2022 • Alexander Buchholz, Jan Malte Lichtenberg, Giuseppe Di Benedetto, Yannik Stein, Vito Bellini, Matteo Ruffini
When adopting the PL model as a ranking policy, both tasks require the computation of expectations with respect to the model.
no code implementations • 28 Jul 2021 • Oriol Barbany Mayor, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Diego Marco Granziol, Matteo Ruffini, Yannik Stein
This paper introduces a method for modeling the probability of an item being seen in different contexts, e. g., for different users, with a single estimator.
no code implementations • 28 Apr 2020 • Giuseppe Di Benedetto, Vito Bellini, Giovanni Zappella
Here we present a contextual bandit algorithm which detects and adapts to abrupt changes of the reward function and leverages previous estimations whenever the environment falls back to a previously observed state.
no code implementations • 30 Mar 2020 • Giuseppe Di Benedetto, François Caron, Yee Whye Teh
In particular, the Indian buffet process is a flexible and simple one-parameter feature allocation model where the number of features grows unboundedly with the number of objects.
no code implementations • 20 Nov 2017 • Giuseppe Di Benedetto, François Caron, Yee Whye Teh
Along with this result, we provide the asymptotic behaviour of the number of clusters of a given size, and show that the model can exhibit a power-law behavior, controlled by another parameter.