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Found 8 papers, 4 papers with code
Sequential Counterfactual Risk Minimization
Counterfactual Risk Minimization (CRM) is a framework for dealing with the logged bandit feedback problem, where the goal is to improve a logging policy using offline data.
Efficient Kernel UCB for Contextual Bandits
While standard methods require a O(CT^3) complexity where T is the horizon and the constant C is related to optimizing the UCB rule, we propose an efficient contextual algorithm for large-scale problems.
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.
A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling
Individual Treatment Effect (ITE) prediction is an important area of research in machine learning which aims at explaining and estimating the causal impact of an action at the granular level.
Treatment Targeting by AUUC Maximization with Generalization Guarantees
In real life scenarios, when we do not have access to ground-truth individual treatment effect, the capacity of models to do so is generally measured by the Area Under the Uplift Curve (AUUC), a metric that differs from the learning objectives of most of the Individual Treatment Effect (ITE) models.
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains.
Counterfactual Learning of Stochastic Policies with Continuous Actions: from Models to Offline Evaluation
Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare.
Attribution Modeling Increases Efficiency of Bidding in Display Advertising
Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry.
