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Found 3 papers, 1 papers with code
Lipschitzness Is All You Need To Tame Off-policy Generative Adversarial Imitation Learning
Despite the recent success of reinforcement learning in various domains, these approaches remain, for the most part, deterringly sensitive to hyper-parameters and are often riddled with essential engineering feats allowing their success.
Learning by stochastic serializations
In this paper, we propose to map any complex structure onto a generic form, called serialization, over which we can apply any sequence-based density estimator.
Regularising Non-linear Models Using Feature Side-information
In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance.
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