research
∙
09/08/2023
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become...
share
research
∙
08/26/2023
i-Align: an interpretable knowledge graph alignment model
Knowledge graphs (KGs) are becoming essential resources for many downstr...
research
∙
04/19/2023
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions betwee...
research
∙
04/18/2023
Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis of Micro-mobility Services
Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly pop...
research
∙
04/15/2023
More Is Less: When Do Recommenders Underperform for Data-rich Users?
Users of recommender systems tend to differ in their level of interactio...
research
∙
10/14/2022
Analysing Donors' Behaviour in Non-profit Organisations for Disaster Resilience: The 2019–2020 Australian Bushfires Case Study
With the advancement and proliferation of technology, non-profit organis...
research
∙
07/11/2022
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
With the introduction of machine learning in high-stakes decision making...
research
∙
05/31/2022
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems
Model-free deep-reinforcement-based learning algorithms have been applie...
research
∙
03/14/2022
Ethical and Fairness Implications of Model Multiplicity
While predictive models are a purely technological feat, they may operat...
research
∙
02/10/2022
Measuring disentangled generative spatio-temporal representation
Disentangled representation learning offers useful properties such as di...
research
∙
09/17/2021
Learning Enhanced Optimisation for Routing Problems
Deep learning approaches have shown promising results in solving routing...
research
∙
06/10/2021
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and su...
research
∙
05/21/2021
Parallelizing Contextual Linear Bandits
Standard approaches to decision-making under uncertainty focus on sequen...
research
∙
12/24/2020
Learning Vehicle Routing Problems using Policy Optimisation
Deep reinforcement learning (DRL) has been used to learn effective heuri...
research
∙
12/04/2020
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
The predict+optimize problem combines machine learning ofproblem coeffic...
research
∙
11/30/2020
Representing and Denoising Wearable ECG Recordings
Modern wearable devices are embedded with a range of noninvasive biomark...
research
∙
10/23/2020
Learning to Optimise General TSP Instances
The Travelling Salesman Problem (TSP) is a classical combinatorial optim...
research
∙
08/26/2020
Joint Modelling of Cyber Activities and Physical Context to Improve Prediction of Visitor Behaviors
This paper investigates the Cyber-Physical behavior of users in a large ...
research
∙
07/24/2020
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Decision tree learning is a widely used approach in machine learning, fa...
research
∙
07/09/2020
Less is More: Rejecting Unreliable Reviews for Product Question Answering
Promptly and accurately answering questions on products is important for...
research
∙
05/27/2020
An Ambient-Physical System to Infer Concentration in Open-plan Workplace
One of the core challenges in open-plan workspaces is to ensure a good l...
research
∙
11/05/2019
Flight Delay Prediction using Airport Situational Awareness Map
The prediction of flight delays plays a significantly important role for...
research
∙
07/27/2019
Alternative Blockmodelling
Many approaches have been proposed to discover clusters within networks....
research
∙
03/08/2019
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Deep learning has been extended to a number of new domains with critical...
research
∙
02/16/2018
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Inference for population genetics models is hindered by computationally ...
research
∙
06/17/2016