25 citations · 119 across the 42 of their papers we have counts for
8 papers · 1 filter
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
Thommen George Karimpanal, Hung Le, Majid Abdolshah +4
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard learning. Such a bias fails to account for…
A Field Guide to Scientific XAI: Transparent and Interpretable Deep Learning for Bioinformatics Research
Thomas P Quinn, Sunil Gupta, Svetha Venkatesh +1
Deep learning has become popular because of its potential to achieve high accuracy in prediction tasks. However, accuracy is not always the only goal of statistical modelling, espe…
Plug and Play, Model-Based Reinforcement Learning
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce P…
Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support
Hung Tran-The, Sunil Gupta, Thanh Nguyen-Tang +2
We address policy learning with logged data in contextual bandits. Current offline-policy learning algorithms are mostly based on inverse propensity score (IPS) weighting requiring…
A New Representation of Successor Features for Transfer across Dissimilar Environments
Majid Abdolshah, Hung Le, Thommen Karimpanal George +3
Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function ch…
Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana +1
Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bou…