7 citations · 9 across the 2 of their papers we have counts for
4 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…
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…
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Piali Das, Valerio Perrone, Nikita Ivkin +22
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of…
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models
Young Jun Ko, Matthias Seeger
Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of im…