83 citations · 110 across the 7 of their papers we have counts for
7 papers
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo +2
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data. A critical challenge of this approach i…
Multi-timestep models for Model-based Reinforcement Learning
Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas +2
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data. A critical challenge of this approach i…
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa +2
This paper investigates when the importance weighting (IW) correction is needed to address covariate shift, a common situation in supervised learning where the input distributions…
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt +1
Autoencoders and their variants are among the most widely used models in representation learning and generative modeling. However, autoencoder-based models usually assume that the…
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
Karl Krauth, Edwin V. Bonilla, Kurt Cutajar +1
We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference;…
Random Feature Expansions for Deep Gaussian Processes
Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi +1
The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning prob…