Search Results for author:
Found 29 papers, 8 papers with code
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy
To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high likelihood of containing the global optimum, and then use BO to optimize the objective function within these regions.
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images.
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable.
i-Align: an interpretable knowledge graph alignment model
One of the strategies to address this problem is KG alignment, i. e., forming a more complete KG by merging two or more KGs.
Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
Group fairness is achieved by equalising prediction distributions between protected sub-populations; individual fairness requires treating similar individuals alike.
More Is Less: When Do Recommenders Underperform for Data-rich Users?
Users of recommender systems tend to differ in their level of interaction with these algorithms, which may affect the quality of recommendations they receive and lead to undesirable performance disparity.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies
Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem.
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems
In this paper, we show that our model can generalise to various route problems, such as the split-delivery VRP (SDVRP), and compare the performance of our method with that of current state-of-the-art approaches.
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity
While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences.
Measuring disentangled generative spatio-temporal representation
Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches.
Spatio-temporal Disentangled representation learning for mobility prediction
In this work, we propose a VAE-based architecture for learning the disentangled representation from real spatio-temporal data for mobility forecasting.
Learning Enhanced Optimisation for Routing Problems
However, there is still a substantial gap in solution quality between machine learning and operations research algorithms.
MoParkeR : Multi-objective Parking Recommendation
Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only.
Parallelizing Contextual Bandits
Standard approaches to decision-making under uncertainty focus on sequential exploration of the space of decisions.
Detecting Singleton Spams in Reviews via Learning Deep Anomalous Temporal Aspect-Sentiment Patterns
Meanwhile, opinion spams spread widely and the detection of spam reviews becomes critically important for ensuring the integrity of the echo system of online reviews.
Learning Vehicle Routing Problems using Policy Optimisation
In our evaluation, we experimentally illustrate that the model produces state-of-the-art performance on variants of Vehicle Routing problems such as Capacitated Vehicle Routing Problem (CVRP), Multiple Routing with Fixed Fleet Problems (MRPFF) and Travelling Salesman problem.
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
We compare our approach withother approaches to the predict+optimize problem and showwe can successfully tackle some hard combinatorial problemsbetter than other predict+optimize methods.
Representing and Denoising Wearable ECG Recordings
In this work, we develop a statistical model to simulate a structured noise process in ECGs derived from a wearable sensor, design a beat-to-beat representation that is conducive for analyzing variation, and devise a factor analysis-based method to denoise the ECG.
Learning to Optimise General TSP Instances
In recent years, learning to optimise approaches have shown success in solving TSP problems.
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 indoor shopping mall by leveraging anonymized (opt in) Wi-Fi association and browsing logs recorded by the mall operators.
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
Less is More: Rejecting Unreliable Reviews for Product Question Answering
In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question.
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 level of concentration for the workers while performing their tasks.
Exchangeable Variational Autoencoders with Applications to Genomic Data
Exchangeable-structured datapoints are ubiquitous in statistical problems ranging from point clouds to graphs to sets.
Alternative Blockmodelling
Secondly, blockmodelling is a summary representation of a network which regards not only membership of nodes but also relations between clusters.
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 success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and its variants are not commonly solved using such techniques.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables.
Ground Truth Bias in External Cluster Validity Indices
For example, NCinc bias in the RI can be changed to NCdec bias by skewing the distribution of clusters in the ground truth partition.
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions.
