Publications (23)
TraceNAS: Zero-shot LLM Pruning via Gradient Trace Correlation
Prajna G. Malettira, Manish Nagaraj, Arjun Roy +2
Structured pruning is essential for efficient deployment of Large Language Models (LLMs). The varying sensitivity of LLM sub-blocks to pruning necessitates the identification of op…
Learning to Teach Fairness-aware Deep Multi-task Learning
Arjun Roy, Eirini Ntoutsi
Fairness-aware learning mainly focuses on single task learning (STL). The fairness implications of multi-task learning (MTL) have only recently been considered and a seminal approa…
The Easy Path to Robustness: Coreset Selection using Sample Hardness
Pranav Ramesh, Arjun Roy, Deepak Ravikumar +2
Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset sel…
Achieving Hilbert-Schmidt Independence Under Rényi Differential Privacy for Fair and Private Data Generation
Tobias Hyrup, Emmanouil Panagiotou, Arjun Roy +3
As privacy regulations such as the GDPR and HIPAA and responsibility frameworks for artificial intelligence such as the AI Act gain traction, the ethical and responsible use of rea…
Multi-fairness under class-imbalance
Arjun Roy, Vasileios Iosifidis, Eirini Ntoutsi
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced.…
Damage Rate Laws and Failure Statistics for Lumped Coupled-Field Systems via Averaging
Arjun Roy, Joseph P. Cusumano
We study the non-linear dynamics and failure statistics of a coupled-field fatigue damage evolution model. We develop a methodology to derive averaged damage evolution rate laws fr…