111 citations · 282 across the 52 of their papers we have counts for
10 papers · 1 filter
Bi-directional Model Cascading with Proxy Confidence
David Warren, Mark Dras
Model Cascading, recently applied successfully to LLMs, is a simple but powerful technique that improves the efficiency of inference by selectively applying models of varying sizes…
Empirical Calibration and Metric Differential Privacy in Language Models
Pedro Faustini, Natasha Fernandes, Annabelle McIver +1
NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the privacy budget . However, doe…
Comparing privacy notions for protection against reconstruction attacks in machine learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy pres…
Graded Suspiciousness of Adversarial Texts to Human
Shakila Mahjabin Tonni, Pedro Faustini, Mark Dras
Adversarial examples pose a significant challenge to deep neural networks (DNNs) across both image and text domains, with the intent to degrade model performance through meticulous…
Bayes' capacity as a measure for reconstruction attacks in federated learning
Sayan Biswas, Mark Dras, Pedro Faustini +4
Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy…
Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients
Weijun Li, Qiongkai Xu, Mark Dras
Recent studies have shown that distributed machine learning is vulnerable to gradient inversion attacks, where private training data can be reconstructed by analyzing the gradients…
What Learned Representations and Influence Functions Can Tell Us About Adversarial Examples
Shakila Mahjabin Tonni, Mark Dras
Adversarial examples, deliberately crafted using small perturbations to fool deep neural networks, were first studied in image processing and more recently in NLP. While approaches…
OptIForest: Optimal Isolation Forest for Anomaly Detection
Haolong Xiang, Xuyun Zhang, Hongsheng Hu +5
Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health…
Directional Privacy for Deep Learning
Pedro Faustini, Natasha Fernandes, Shakila Tonni +2
Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying privacy in the training of deep learning models. It applies isotropic Gaussian noise to gra…
Deep reinforcement learning guided graph neural networks for brain network analysis
Xusheng Zhao, Jia Wu, Hao Peng +9
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or co…