78 citations · 354 across the 35 of their papers we have counts for
12 papers · 1 filter
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
Ali Ugur Guler, Emir Demirovic, Jeffrey Chan +3
The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan…
Improving Scalability of Contrast Pattern Mining for Network Traffic Using Closed Patterns
Elaheh AlipourChavary, Sarah M. Erfani, Christopher Leckie
Contrast pattern mining (CPM) aims to discover patterns whose support increases significantly from a background dataset compared to a target dataset. CPM is particularly useful for…
Defending Distributed Classifiers Against Data Poisoning Attacks
Sandamal Weerasinghe, Tansu Alpcan, Sarah M. Erfani +1
Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations such as poisoning attacks and label flips. By carefully manipulating a subset of training samp…
Defending Regression Learners Against Poisoning Attacks
Sandamal Weerasinghe, Sarah M. Erfani, Tansu Alpcan +2
Regression models, which are widely used from engineering applications to financial forecasting, are vulnerable to targeted malicious attacks such as training data poisoning, throu…
Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
Yi Han, Shanika Karunasekera, Christopher Leckie
Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, re…
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams
Amila Silva, Shanika Karunasekera, Christopher Leckie +1
Many learning tasks involve multi-modal data streams, where continuous data from different modes convey a comprehensive description about objects. A major challenge in this context…