5 citations · 5 across the 4 of their papers we have counts for
5 papers
Exploiting Similarities in A/B Testing with Off-Policy Estimation
Otmane Sakhi, Alexandre Gilotte, David Rohde
We study A/B testing, the standard protocol for measuring the performance gain of a new decision system relative to a baseline. Traditional A/B testing treats both systems as black…
Learning from aggregated data with a maximum entropy model
Alexandre Gilotte, Ahmed Ben Yahmed, David Rohde
Aggregating a dataset, then injecting some noise, is a simple and common way to release differentially private data.However, aggregated data -- even without noise -- is not an appr…
Fast Offline Policy Optimization for Large Scale Recommendation
Otmane Sakhi, David Rohde, Alexandre Gilotte
Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of thes…
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…
Ranking metrics on non-shuffled traffic
Alexandre Gilotte
Ranking metrics are a family of metrics largely used to evaluate recommender systems. However they typically suffer from the fact the reward is affected by the order in which recom…