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Found 10 papers, 5 papers with code
Kernel Density Bayesian Inverse Reinforcement Learning
Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well.
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.
Spectral embedding for dynamic networks with stability guarantees
We consider the problem of embedding a dynamic network, to obtain time-evolving vector representations of each node, which can then be used to describe changes in behaviour of individual nodes, communities, or the entire graph.
Neural Lumigraph Rendering
Novel view synthesis is a challenging and ill-posed inverse rendering problem.
Probabilistic Contrastive Principal Component Analysis
Recently, contrastive principal component analysis (CPCA) was proposed for this setting.
The multilayer random dot product graph
We present a comprehensive extension of the latent position network model known as the random dot product graph to accommodate multiple graphs -- both undirected and directed -- which share a common subset of nodes, and propose a method for jointly embedding the associated adjacency matrices, or submatrices thereof, into a suitable latent space.
Spectral embedding of weighted graphs
When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results.
Mesoscopic Facial Geometry Inference Using Deep Neural Networks
We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.
