250 citations · 532 across the 72 of their papers we have counts for
10 papers · 1 filter
Decomposing Task Vectors for Refined Model Editing
Hamed Damirchi, Ehsan Abbasnejad, Zhen Zhang +1
Large pre-trained models have transformed machine learning, yet adapting these models effectively to exhibit precise, concept-specific behaviors remains a significant challenge. Ta…
The Quest for Winning Tickets in Low-Rank Adapters
Hamed Damirchi, Cristian Rodriguez-Opazo, Ehsan Abbasnejad +2
The Lottery Ticket Hypothesis (LTH) suggests that over-parameterized neural networks contain sparse subnetworks ("winning tickets") capable of matching full model performance when…
Certified but Fooled! Breaking Certified Defences with Ghost Certificates
Quoc Viet Vo, Tashreque M. Haq, Paul Montague +3
Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarante…
CEDL: Centre-Enhanced Discriminative Learning for Anomaly Detection
Zahra Zamanzadeh Darban, Qizhou Wang, Charu C. Aggarwal +3
Supervised anomaly detection methods perform well in identifying known anomalies that are well represented in the training set. However, they often struggle to generalise beyond th…
Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection
I. M. De la Jara, C. Rodriguez-Opazo, D. Teney +2
Out-of-distribution (OOD) detection is essential for reliably deploying machine learning models in the wild. Yet, most methods treat large pre-trained models as monolithic encoders…
Beyond Imitation: Recovering Dense Rewards from Demonstrations
Jiangnan Li, Thuy-Trang Vu, Ehsan Abbasnejad +1
Conventionally, supervised fine-tuning (SFT) is treated as a simple imitation learning process that only trains a policy to imitate expert behavior on demonstration datasets. In th…