20 citations · 57 across the 5 of their papers we have counts for
4 papers · 1 filter
Robust Imitation Learning from Noisy Demonstrations
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation lear…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…
Vprop: Variational Inference using RMSprop
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt +1
Many computationally-efficient methods for Bayesian deep learning rely on continuous optimization algorithms, but the implementation of these methods requires significant changes t…
Variational Adaptive-Newton Method for Explorative Learning
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt +2
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and rein…