19 citations · 22 across the 5 of their papers we have counts for
10 papers
From Clicks to Conversions: Recommendation for long-term reward
Philomène Chagniot, Flavian Vasile, David Rohde
Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e.g. a click) can be observed immediately after the recommenda…
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
Otmane Sakhi, Stephen Bonner, David Rohde +1
A common task for recommender systems is to build a pro le of the interests of a user from items in their browsing history and later to recommend items to the user from the same ca…
Causal inference with Bayes rule
Finnian Lattimore, David Rohde
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally n…
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
Otmane Sakhi, Stephen Bonner, David Rohde +1
The combination of the re-parameterization trick with the use of variational auto-encoders has caused a sensation in Bayesian deep learning, allowing the training of realistic gene…
Learning from Bandit Feedback: An Overview of the State-of-the-art
Olivier Jeunen, Dmytro Mykhaylov, David Rohde +3
In machine learning we often try to optimise a decision rule that would have worked well over a historical dataset; this is the so called empirical risk minimisation principle. In…
Recommendation System-based Upper Confidence Bound for Online Advertising
Nhan Nguyen-Thanh, Dana Marinca, Kinda Khawam +5
In this paper, the method UCB-RS, which resorts to recommendation system (RS) for enhancing the upper-confidence bound algorithm UCB, is presented. The proposed method is used for…