3 papers
cs.LG2026
LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators
Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo +6
Generative discovery in science requires coupling high-level scientific reasoning with the ability to generate samples respecting the structure of a design space. Yet these capabil…
cs.LG2026
Fast Unlearning at Scale via Margin Self-Correction
Federico Di Gennaro, Alexander Shevchenko, Fanny Yang
Language-model unlearning updates a trained model to behave as if it had not seen selected training examples, while preserving utility and avoiding costly retraining. Existing appr…
stat.ML2026
Hedging on the Frontier: Learning New Tasks with Few Samples
Tobias Wegel, Federico Di Gennaro, Geelon So +1
When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks i…