280 citations · 2k across the 81 of their papers we have counts for
6 papers · 1 filter
Variational Continual Learning
Cuong V. Nguyen, Yingzhen Li, Thang D. Bui +1
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in…
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning
Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani +3
Off-policy model-free deep reinforcement learning methods using previously collected data can improve sample efficiency over on-policy policy gradient techniques. On the other hand…
Discriminative k-shot learning using probabilistic models
Matthias Bauer, Mateo Rojas-Carulla, Jakub Bartłomiej Świątkowski +2
This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task compri…
Streaming Sparse Gaussian Process Approximations
Thang D. Bui, Cuong V. Nguyen, Richard E. Turner
Sparse pseudo-point approximations for Gaussian process (GP) models provide a suite of methods that support deployment of GPs in the large data regime and enable analytic intractab…
Gradient Estimators for Implicit Models
Yingzhen Li, Richard E. Turner
Implicit models, which allow for the generation of samples but not for point-wise evaluation of probabilities, are omnipresent in real-world problems tackled by machine learning an…
Approximate Inference with Amortised MCMC
Yingzhen Li, Richard E. Turner, Qiang Liu
We propose a novel approximate inference algorithm that approximates a target distribution by amortising the dynamics of a user-selected MCMC sampler. The idea is to initialise MCM…