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Found 13 papers, 1 papers with code
BO-Muse: A human expert and AI teaming framework for accelerated experimental design
In this paper we introduce BO-Muse, a new approach to human-AI teaming for the optimization of expensive black-box functions.
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups
Each learning group consists of a teacher network, a student network and a novel filter module.
Learning to Constrain Policy Optimization with Virtual Trust Region
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses a virtual trust region to regulate each policy update.
Episodic Policy Gradient Training
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly.
Balanced Q-learning: Combining the Influence of Optimistic and Pessimistic Targets
The optimistic nature of the Q-learning target leads to an overestimation bias, which is an inherent problem associated with standard $Q-$learning.
Model-Based Episodic Memory Induces Dynamic Hybrid Controls
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory.
Neural Latent Traversal with Semantic Constraints
Whilst Generative Adversarial Networks (GANs) generate visually appealing high resolution images, the latent representations (or codes) of these models do not allow controllable changes on the semantic attributes of the generated images.
Reachability Traces for Curriculum Design in Reinforcement Learning
The objective in goal-based reinforcement learning is to learn a policy to reach a particular goal state within the environment.
Plug and Play, Model-Based Reinforcement Learning
This is achieved by representing the global transition dynamics as a union of local transition functions, each with respect to one active object in the scene.
A New Representation of Successor Features for Transfer across Dissimilar Environments
Transfer in reinforcement learning is usually achieved through generalisation across tasks.
Cost-aware Multi-objective Bayesian optimisation
We introduce a cost-aware multi-objective Bayesian optimisation with non-uniform evaluation cost over objective functions by defining cost-aware constraints over the search space.
Stable Bayesian Optimisation via Direct Stability Quantification
In this paper we consider the problem of finding stable maxima of expensive (to evaluate) functions.
Multi-objective Bayesian optimisation with preferences over objectives
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B".
