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09/20/2023
It's Simplex! Disaggregating Measures to Improve Certified Robustness
Certified robustness circumvents the fragility of defences against adver...
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08/15/2023
Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks
Poisoning attacks can disproportionately influence model behaviour by ma...
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06/22/2023
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks suc...
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02/09/2023
Exploiting Certified Defences to Attack Randomised Smoothing
In guaranteeing that no adversarial examples exist within a bounded regi...
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12/22/2022
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
Quantum machine learning (QML) has received increasing attention due to ...
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11/23/2022
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rap...
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10/12/2022
Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
In response to subtle adversarial examples flipping classifications of n...
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06/16/2022
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm
Quantum computing is anticipated to offer immense computational capabili...
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09/24/2021
Local Intrinsic Dimensionality Signals Adversarial Perturbations
The vulnerability of machine learning models to adversarial perturbation...
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02/15/2021
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences
Event suffix and remaining time prediction are sequence to sequence lear...
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01/01/2021
Neural Architecture Search via Combinatorial Multi-Armed Bandit
Neural Architecture Search (NAS) has gained significant popularity as an...
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11/16/2020
Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns
Contrast pattern mining (CPM) aims to discover patterns whose support in...
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08/21/2020
Defending Distributed Classifiers Against Data Poisoning Attacks
Support Vector Machines (SVMs) are vulnerable to targeted training data ...
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08/21/2020
Defending Regression Learners Against Poisoning Attacks
Regression models, which are widely used from engineering applications t...
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06/07/2018
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels ...
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01/08/2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Deep Neural Networks (DNNs) have recently been shown to be vulnerable ag...
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01/08/2018
Online Cluster Validity Indices for Streaming Data
Cluster analysis is used to explore structure in unlabeled data sets in ...
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07/28/2017
