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cs.LG2026
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…
cs.LG2026
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, but composing multiple custom concepts remains challenging due to representation int…
cs.LG2025
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…