73 citations · 272 across the 33 of their papers we have counts for
36 papers
Optimizing Hyperparameters with Conformal Quantile Regression
David Salinas, Jacek Golebiowski, Aaron Klein +2
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian p…
Renate: A Library for Real-World Continual Learning
Martin Wistuba, Martin Ferianc, Lukas Balles +2
Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indicati…
Fortuna: A Library for Uncertainty Quantification in Deep Learning
Gianluca Detommaso, Alberto Gasparin, Michele Donini +3
We present Fortuna, an open-source library for uncertainty quantification in deep learning. Fortuna supports a range of calibration techniques, such as conformal prediction that ca…
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore +6
We provide a differentially private algorithm for producing synthetic data simultaneously useful for multiple tasks: marginal queries and multitask machine learning (ML). A key inn…
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors
Gianluca Detommaso, Alberto Gasparin, Andrew Wilson +1
As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce…
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
Ondrej Bohdal, Lukas Balles, Martin Wistuba +3
Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly…