2 code implementations • 7 Jul 2020 • Yi Han, Shanika Karunasekera, Christopher Leckie
(2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection.
1 code implementation • 16 Feb 2015 • Sergey Demyanov, James Bailey, Ramamohanarao Kotagiri, Christopher Leckie
In many classification problems a classifier should be robust to small variations in the input vector.
1 code implementation • 6 Jul 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep neural network classifiers suffer from adversarial vulnerability: well-crafted, unnoticeable changes to the input data can affect the classifier decision.
1 code implementation • NeurIPS 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Deep learning classifiers are susceptible to well-crafted, imperceptible variations of their inputs, known as adversarial attacks.
1 code implementation • 2 Dec 2022 • Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie
In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions.
1 code implementation • 15 Jan 2020 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
The significant advantage of such models is their easy-to-compute inverse.
2 code implementations • 1 Dec 2021 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
Neural networks are vulnerable to adversarial attacks: adding well-crafted, imperceptible perturbations to their input can modify their output.
1 code implementation • 13 Sep 2022 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
By leveraging the theory of coreset selection, we show how selecting a small subset of training data provides a principled approach to reducing the time complexity of robust training.
1 code implementation • 21 Aug 2020 • Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary.
no code implementations • 8 Jan 2018 • Masud Moshtaghi, James C. Bezdek, Sarah M. Erfani, Christopher Leckie, James Bailey
An important part of cluster analysis is validating the quality of computationally obtained clusters.
no code implementations • 28 Jul 2017 • Tansu Alpcan, Sarah M. Erfani, Christopher Leckie
After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research.
no code implementations • 21 Sep 2016 • Tansu Alpcan, Benjamin I. P. Rubinstein, Christopher Leckie
Such high-dimensional decision spaces and big data sets lead to computational challenges, relating to efforts in non-linear optimization scaling up to large systems of variables.
no code implementations • 3 Aug 2016 • Fateme Fahiman, Jame C. Bezdek, Sarah M. Erfani, Christopher Leckie, Marimuthu Palaniswami
The two new algorithms are heuristic derivatives of fuzzy c-means (FCM).
no code implementations • 17 Aug 2018 • Yi Han, Benjamin I. P. Rubinstein, Tamas Abraham, Tansu Alpcan, Olivier De Vel, Sarah Erfani, David Hubczenko, Christopher Leckie, Paul Montague
Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification.
no code implementations • 7 Nov 2018 • Minh Tuan Doan, Jianzhong Qi, Sutharshan Rajasegarar, Christopher Leckie
Subspace clustering aims to find groups of similar objects (clusters) that exist in lower dimensional subspaces from a high dimensional dataset.
no code implementations • ICLR 2018 • Prameesha Sandamal Weerasinghe, Tansu Alpcan, Sarah Monazam Erfani, Christopher Leckie
Anomaly detection discovers regular patterns in unlabeled data and identifies the non-conforming data points, which in some cases are the result of malicious attacks by adversaries.
no code implementations • 25 Feb 2019 • Yi Han, David Hubczenko, Paul Montague, Olivier De Vel, Tamas Abraham, Benjamin I. P. Rubinstein, Christopher Leckie, Tansu Alpcan, Sarah Erfani
Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting.
no code implementations • 23 Oct 2019 • Amila Silva, Shanika Karunasekera, Christopher Leckie, Ling Luo
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics.
no code implementations • 11 Feb 2020 • Yi Han, Shanika Karunasekera, Christopher Leckie
Events detected from social media streams often include early signs of accidents, crimes or disasters.
no code implementations • 18 Jun 2020 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations.
no code implementations • 23 Jul 2020 • Amila Silva, Shanika Karunasekera, Christopher Leckie, Ling Luo
To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams.
1 code implementation • 24 Jul 2020 • Emir Demirović, Anna Lukina, Emmanuel Hebrard, Jeffrey Chan, James Bailey, Christopher Leckie, Kotagiri Ramamohanarao, Peter J. Stuckey
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
1 code implementation • 21 Aug 2020 • Sandamal Weerasinghe, Sarah M. Erfani, Tansu Alpcan, Christopher Leckie, Justin Kopacz
Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, through which adversaries can manipulate their predictions.
no code implementations • 16 Nov 2020 • Elaheh AlipourChavary, Sarah M. Erfani, Christopher Leckie
In addition, as an application of CPs, we demonstrate that CPM is a highly effective method for detection of meaningful changes in network traffic.
no code implementations • 4 Dec 2020 • Ali Ugur Guler, Emir Demirovic, Jeffrey Chan, James Bailey, Christopher Leckie, Peter J. Stuckey
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.
no code implementations • 11 Feb 2021 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost.
1 code implementation • 24 Sep 2021 • Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein
In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, has a positive correlation with the magnitude of the perturbation.
no code implementations • 29 Sep 2021 • Siqi Xia, Shijie Liu, Trung Le, Dinh Phung, Sarah Erfani, Benjamin I. P. Rubinstein, Christopher Leckie, Paul Montague
More specifically, by minimizing the WS distance of interest, an adversarial example is pushed toward the cluster of benign examples sharing the same label on the latent space, which helps to strengthen the generalization ability of the classifier on the adversarial examples.
1 code implementation • 13 Oct 2022 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
We show the effectiveness of the proposed method for robust training of DNNs on various poisoned datasets, reducing the backdoor success rate significantly.
no code implementations • 23 Nov 2022 • Maxwell T. West, Sarah M. Erfani, Christopher Leckie, Martin Sevior, Lloyd C. L. Hollenberg, Muhammad Usman
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
1 code implementation • 15 Mar 2023 • Hadi M. Dolatabadi, Sarah Erfani, Christopher Leckie
In particular, we leverage the power of diffusion models and show that a carefully designed denoising process can counteract the effectiveness of the data-protecting perturbations.
no code implementations • 23 Mar 2023 • Marc Katzef, Andrew C. Cullen, Tansu Alpcan, Christopher Leckie, Justin Kopacz
When such failures arise in wireless communications networks, important services that they use/provide (like anomaly detection) can be left inoperable and can result in a cascade of security problems.
no code implementations • 18 May 2023 • Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
no code implementations • 22 Jun 2023 • Maxwell T. West, Shu-Lok Tsang, Jia S. Low, Charles D. Hill, Christopher Leckie, Lloyd C. L. Hollenberg, Sarah M. Erfani, Muhammad Usman
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
no code implementations • 12 Nov 2023 • Qizhou Wang, Guansong Pang, Mahsa Salehi, Christopher Leckie
However, current methods tend to over-emphasise fitting the seen anomalies, leading to a weak generalisation ability to detect unseen anomalies, i. e., those that are not illustrated by the labelled anomaly nodes.
no code implementations • 7 Feb 2024 • Chen Wang, Sarah Erfani, Tansu Alpcan, Christopher Leckie
Our offline learning model is an adaptation of behavioural cloning with a transformer policy network, where we modify the training process to learn a Q function and a state value function from normal trajectories.
no code implementations • 17 Feb 2024 • Hadi M. Dolatabadi, Sarah M. Erfani, Christopher Leckie
Our analysis of these two failure cases of DNNs reveals that finding a unified solution for shortcut learning in DNNs is not out of reach, and TDA can play a significant role in forming such a framework.
1 code implementation • 21 Feb 2024 • Canaan Yung, Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie
To address this issue, we propose the Round Trip Translation (RTT) method, the first algorithm specifically designed to defend against social-engineered attacks on LLMs.