Existing LLM unlearning methods struggle with robustness: unbounded forget losses degrade model coherence, fixed-weight balancing cannot adapt as retain difficulty shifts mid-training, and methods that work on one benchmark falter under scaling or repeated application. We propose BLADE, a constrained bilevel framework...
Md Toufikuzzaman, Ahmad Mousavi, Dongwon Lee· 0 citations
The results presented show that predictive performance, operational trustworthiness, and computational practicality need not be competing objectives in next-day active-fire prediction.
Miguel Esparza, Aydin Ayanzadeh, Ahmad Mousavi et al.· 0 citations
We develop a spectral three-term modification of the classic Hestenes--Stiefel conjugate gradient algorithm, preserving its anti-jamming characteristic and, simultaneously, taking care of the sufficient descent property. We discuss how a modified secant equation can be extracted from our modification scheme, yielding a...
Saman Babaie-Kafaki, Maryam Khoshsimaye-Bargard, Ahmad Mousavi· 0 citations
A unified, careful framework and a set of quantitative consequences for fixed-rank tractability results are claimed, rather than a claim to originate the known fixed-rank tractability results that motivate it.
M. Soltanalian, Ahmad Mousavi· 1 citation
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