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Found 7 papers, 1 papers with code
Repeated Bidding with Dynamic Value
We consider a repeated auction where the buyer's utility for an item depends on the time that elapsed since his last purchase.
Learning from aggregated data with a maximum entropy model
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 appropriate input for machine learning classifiers. In this work, we show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
Fast Offline Policy Optimization for Large Scale Recommendation
Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context.
Lessons from the AdKDD'21 Privacy-Preserving ML Challenge
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry.
Learning from Bandit Feedback: An Overview of the State-of-the-art
In order to handle this "bandit-feedback" setting, several Counterfactual Risk Minimisation (CRM) methods have been proposed in recent years, that attempt to estimate the performance of different policies on historical data.
Ranking metrics on non-shuffled traffic
Ranking metrics are a family of metrics largely used to evaluate recommender systems.
Offline A/B testing for Recommender Systems
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data.
