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Found 8 papers, 0 papers with code
Fast Slate Policy Optimization: Going Beyond Plackett-Luce
We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.
PAC-Bayesian Offline Contextual Bandits With Guarantees
This paper introduces a new principled approach for off-policy learning in contextual bandits.
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users.
Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.
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.
Improving Offline Contextual Bandits with Distributional Robustness
Our approach relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework.
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task.
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
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 generative models of images and has considerably increased our ability to use scalable latent variable models.
