Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

1 code implementation • 23 Oct 2019 • Sanjay Thakur, Herke van Hoof, Gunshi Gupta, David Meger

PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations.

Variational Inference

Time2Vec: Learning a Vector Representation of Time

6 code implementations • 11 Jul 2019 • Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, Marcus Brubaker

Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.

Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks

1 code implementation • 13 Mar 2019 • Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger

Learned controllers such as neural networks typically do not have a notion of uncertainty that allows to diagnose an offset between training and testing conditions, and potentially intervene.