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Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation
To this end, we train a farsighted recommender by using an offline RL algorithm with the policy network in our model architecture that has been initialized from a pre-trained transformer model.
Maximum Mean Discrepancy for Generalization in the Presence of Distribution and Missingness Shift
Covariate shifts are a common problem in predictive modeling on real-world problems.
Estimation of Corporate Greenhouse Gas Emissions via Machine Learning
By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions.
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