5 papers
Adversarial Training of Linear Models under Stealthy Attacks
Lovisa Eriksson, Dave Zachariah, André M. H. Teixeira
Predictive models are widely used in many fields, but are vulnerable to false data injection attacks. To address this, detection schemes and adversarial training have been proposed…
A Robust Optimization Approach to Sparse Principal Component Analysis
David Vävinggren, Francis Bach, André M. H. Teixeira +2
While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dimensional data. Existing method…
SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Javad Parsa, Enis Simsar, Amir Joudaki +2
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models to user-provided concepts (objects, people, or styles), but composing multiple such c…
Byzantine-Robust Federated Learning with Learnable Aggregation Weights
Javad Parsa, Amir Hossein Daghestani, André M. H. Teixeira +1
Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicious (Byzantine) clients poses sig…
Byzantine-Robust Federated Learning Using Generative Adversarial Networks
Usama Zafar, André M. H. Teixeira, Salman Toor
Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, but its robustness is threatened by Byzantine behaviors such as da…