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Found 11 papers, 3 papers with code
Trajectory VAE for multi-modal imitation
In this paper, we use a generative model to capture different emergent playstyles in an unsupervised manner, enabling the imitation of a diverse range of distinct behaviours.
Additive Gaussian Processes Revisited
Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power.
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning
We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms.
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments.
Causal Bayesian Optimization
This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed.
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation.
Structured Variationally Auto-encoded Optimization
We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.
Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods that build on ideas from Monte Carlo tree search to perform adaptive sampling in a manner that balances exploration with exploitation, ensures consistency, and alleviates pathologies in existing adaptive methods.
Relativistic Monte Carlo
Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
Collaborative Filtering with Side Information: a Gaussian Process Perspective
We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression.
