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Found 5 papers, 1 papers with code
In-context Exploration-Exploitation for Reinforcement Learning
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient optimization.
Automatic Music Playlist Generation via Simulation-based Reinforcement Learning
In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment.
Making Differentiable Architecture Search less local
Neural architecture search (NAS) is a recent methodology for automating the design of neural network architectures.
Group induced graphical lasso allows for discovery of molecular pathways-pathways interactions
In this context, high-level layers may considered as groups of variables interacting in lower-level layers.
Latent Variable Time-varying Network Inference
The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point.
