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Found 6 papers, 1 papers with code
Hyperbolic Deep Reinforcement Learning
We propose a new class of deep reinforcement learning (RL) algorithms that model latent representations in hyperbolic space.
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of the conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy.
Should I send this notification? Optimizing push notifications decision making by modeling the future
In this work we focus on mobile push notifications, where the long term effects of recommender system decisions can be particularly strong.
Challenges and approaches to privacy preserving post-click conversion prediction
In this paper we outline the recent privacy-related changes in the online advertising ecosystem from a machine learning perspective.
Learning to Rank For Push Notifications Using Pairwise Expected Regret
Listwise ranking losses have been widely studied in recommender systems.
An Analysis Of Entire Space Multi-Task Models For Post-Click Conversion Prediction
Industrial recommender systems are frequently tasked with approximating probabilities for multiple, often closely related, user actions.
