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Found 6 papers, 2 papers with code
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation
Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior.
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
CFQI uses a compositional $Q$-value function with separate modules for each task variant, allowing it to take advantage of shared knowledge while learning distinct policies for each variant.
Nested Policy Reinforcement Learning for Clinical Decision Support
Off-policy reinforcement learning (RL) has proven to be a powerful framework for guiding agents' actions in environments with stochastic rewards and unknown or noisy state dynamics.
Probabilistic Contrastive Principal Component Analysis
Recently, contrastive principal component analysis (CPCA) was proposed for this setting.
A Divide and Conquer Strategy for High Dimensional Bayesian Factor Models
Our approach is novel in this regard: it includes all of the $n$ samples in each subproblem and, instead, splits the dimension $p$ into smaller subsets for each subproblem.
Methodology
AdaCluster : Adaptive Clustering for Heterogeneous Data
We compare AdaCluster with EM for a Gaussian mixture model on synthetic data and nine UCI data sets.
