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Found 10 papers, 5 papers with code
Post-Episodic Reinforcement Learning Inference
Our goal is to be able to evaluate counterfactual adaptive policies after data collection and to estimate structural parameters such as dynamic treatment effects, which can be used for credit assignment (e. g. what was the effect of the first period action on the final outcome).
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved.
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
Meanwhile, reinforcement learning (RL) is widely regarded as a promising framework for optimizing long-term engagement in sequential recommendation.
Constrained Reinforcement Learning for Short Video Recommendation
In this paper, we formulate the problem of short video recommendation as a constrained Markov Decision Process (MDP), where platforms want to optimize the main goal of user watch time in long term, with the constraint of accommodating the auxiliary responses of user interactions such as sharing/downloading videos.
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance.
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.
Policy Learning with Adaptively Collected Data
Learning optimal policies from historical data enables personalization in a wide variety of applications including healthcare, digital recommendations, and online education.
Distortion Agnostic Deep Watermarking
Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image.
Confidence Intervals for Policy Evaluation in Adaptive Experiments
In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero.
