18 citations · 35 across the 5 of their papers we have counts for
5 papers
Estimating Gradients for Discrete Random Variables by Sampling without Replacement
Wouter Kool, Herke van Hoof, Max Welling
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. We sho…
Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales
Sanjay Thakur, Herke Van Hoof, Gunshi Gupta +1
Neural Network based controllers hold enormous potential to learn complex, high-dimensional functions. However, they are prone to overfitting and unwarranted extrapolations. PAC Ba…
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera +2
Diversity of environments is a key challenge that causes learned robotic controllers to fail due to the discrepancies between the training and evaluation conditions. Training from…
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Wouter Kool, Herke van Hoof, Max Welling
The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample elements without replacement. We show how to implicitly apply this 'Gumbe…
Policy Search with High-Dimensional Context Variables
Voot Tangkaratt, Herke van Hoof, Simone Parisi +3
Direct contextual policy search methods learn to improve policy parameters and simultaneously generalize these parameters to different context or task variables. However, learning…