3 papers
math.OC2025
Sequential test sampling for stochastic derivative-free optimization
Anjie Ding, Francesco Rinaldi, Luis Nunes Vicente
In many derivative-free optimization algorithms, a sufficient decrease condition decides whether to accept a trial step in each iteration. This condition typically requires that th…
cs.LG2025
Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
Gabriele Sanguin, Arjun Pakrashi, Marco Viola +1
Handling uncertainty is critical for ensuring reliable decision-making in intelligent systems. Modern neural networks are known to be poorly calibrated, resulting in predicted conf…
math.OC2024
Direct-search methods in the year 2025: Theoretical guarantees and algorithmic paradigms
K. J. Dzahini, F. Rinaldi, C. W. Royer +1
Optimizing a function without using derivatives is a challenging paradigm, that precludes from using classical algorithms from nonlinear optimization, and may thus seem intractable…