36 citations · 103 across the 16 of their papers we have counts for
14 papers · 1 filter
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…
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…
Gradient-Matching Coresets for Rehearsal-Based Continual Learning
Lukas Balles, Giovanni Zappella, Cédric Archambeau
The goal of continual learning (CL) is to efficiently update a machine learning model with new data without forgetting previously-learned knowledge. Most widely-used CL methods rel…
Diverse Counterfactual Explanations for Anomaly Detection in Time Series
Deborah Sulem, Michele Donini, Muhammad Bilal Zafar +6
Data-driven methods that detect anomalies in times series data are ubiquitous in practice, but they are in general unable to provide helpful explanations for the predictions they m…
A multi-objective perspective on jointly tuning hardware and hyperparameters
David Salinas, Valerio Perrone, Olivier Cruchant +1
In addition to the best model architecture and hyperparameters, a full AutoML solution requires selecting appropriate hardware automatically. This can be framed as a multi-objectiv…