3 papers
cs.LG2025
How far away are truly hyperparameter-free learning algorithms?
Priya Kasimbeg, Vincent Roulet, Naman Agarwal +4
Despite major advances in methodology, hyperparameter tuning remains a crucial (and expensive) part of the development of machine learning systems. Even ignoring architectural choi…
cs.LG2025
Loss Functions and Operators Generated by f-Divergences
Vincent Roulet, Tianlin Liu, Nino Vieillard +2
The logistic loss (a.k.a. cross-entropy loss) is one of the most popular loss functions used for multiclass classification. It is also the loss function of choice for next-token pr…
cs.LG2025
Joint Learning of Energy-based Models and their Partition Function
Michael E. Sander, Vincent Roulet, Tianlin Liu +1
Energy-based models (EBMs) offer a flexible framework for parameterizing probability distributions using neural networks. However, learning EBMs by exact maximum likelihood estimat…