1 citations · 2 across the 6 of their papers we have counts for
6 papers
Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks
Xinglong Chang, Katharina Dost, Gillian Dobbie +1
The performance of machine learning models depends on the quality of the underlying data. Malicious actors can attack the model by poisoning the training data. Current detectors ar…
Fast Adversarial Label-Flipping Attack on Tabular Data
Xinglong Chang, Gillian Dobbie, Jörg Wicker
Machine learning models are increasingly used in fields that require high reliability such as cybersecurity. However, these models remain vulnerable to various attacks, among which…
Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning
Hongsheng Hu, Xuyun Zhang, Zoran Salcic +3
Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning since it allows multiple clients to collaboratively train a global model without granting…
Challenges in Annotating Datasets to Quantify Bias in Under-represented Society
Vithya Yogarajan, Gillian Dobbie, Timothy Pistotti +2
Recent advances in artificial intelligence, including the development of highly sophisticated large language models (LLM), have proven beneficial in many real-world applications. H…
Neuromodulation Gated Transformer
Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado +4
We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We…
Effectiveness of Debiasing Techniques: An Indigenous Qualitative Analysis
Vithya Yogarajan, Gillian Dobbie, Henry Gouk
An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and…