4 citations · 4 across the 10 of their papers we have counts for
13 papers
PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
Amr Mousa, Rifny Rachman, Neil Karavis +2
Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes…
Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
Rifny Rachman, Bahrul Ilmi Nasution, Josh Tingey +3
Meta-reinforcement learning is a promising approach to multi-objective optimisation because it enables rapid policy adaptation across changing environments and preference settings.…
Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
Alma Rahat, Tinkle Chugh, Jonathan Fieldsend +1
This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rat…
MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
Rifny Rachman, Josh Tingey, Richard Allmendinger +3
Multi-objective reinforcement learning (MORL) is effective for multi-echelon combinatorial supply chain optimisation, where tasks involve high dimensionality, uncertainty, and comp…
Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Bahrul Ilmi Nasution, Mark Elliot, Richard Allmendinger
Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but strugg…
Counterfactual Credit Guided Bayesian Optimization
Qiyu Wei, Haowei Wang, Richard Allmendinger +1
Bayesian optimization has emerged as a prominent methodology for optimizing expensive black-box functions by leveraging Gaussian process surrogates, which focus on capturing the gl…
Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
Rifny Rachman, Josh Tingey, Richard Allmendinger +2
This study develops a generalised multi-objective, multi-echelon supply chain optimisation model with non-stationary markets based on a Markov decision process, incorporating econo…
Axiomatic Equilibrium Selection: The Case of Generic Extensive Form Games
Srihari Govindan, Robert B. Wilson
A solution concept that is a refinement of Nash equilibria selects for each finite game a nonempty collection of closed and connected subsets of Nash equilibria as solutions. We im…
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion
Amr Mousa, Neil Karavis, Michele Caprio +2
Quadrupedal locomotion via Reinforcement Learning (RL) is commonly addressed using the teacher-student paradigm, where a privileged teacher guides a proprioceptive student policy.…
Spatial-Aware Decision-Making with Ring Attractors in Reinforcement Learning Systems
Marcos Negre Saura, Richard Allmendinger, Wei Pan +1
Ring attractors, mathematical models inspired by neural circuit dynamics, provide a biologically plausible mechanism to improve learning speed and accuracy in Reinforcement Learnin…
An adaptive approach to Bayesian Optimization with switching costs
Stefan Pricopie, Richard Allmendinger, Manuel Lopez-Ibanez +3
We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search s…
FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning
Kaled M. Alshmrany, Mohannad Aldughaim, Chenfeng Wei +3
We present FuSeBMC-AI, a test generation tool grounded in machine learning techniques. FuSeBMC-AI extracts various features from the program and employs support vector machine and…
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
Danny Wood, Theodore Papamarkou, Matt Benatan +1
In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and…