3 papers
cs.LG2026
BayesAME: Bayesian Active Model Evaluation
Paula Cordero Encinar, Taylan Cemgil, Arnaud Doucet +2
Evaluating large generative models across benchmarks is time-consuming and computationally expensive. This drives the need for methods that can estimate full benchmark performance…
cs.AI2026
Rich Insights from Cheap Signals: Efficient Evaluations via Tensor Factorization
Felipe Maia Polo, Aida Nematzadeh, Virginia Aglietti +2
Moving beyond evaluations that collapse performance across heterogeneous prompts toward fine-grained evaluation at the prompt level, or within relatively homogeneous subsets, is ne…
cs.LG2024
GradINN: Gradient Informed Neural Network
Filippo Aglietti, Francesco Della Santa, Andrea Piano +1
We propose Gradient Informed Neural Networks (GradINNs), a methodology inspired by Physics Informed Neural Networks (PINNs) that can be used to efficiently approximate a wide range…