495 citations · 1.1k across the 25 of their papers we have counts for
5 papers · 1 filter
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)
Sören Mindermann, Muhammed Razzak, Winnie Xu +7
We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acqu…
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian +3
We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning. Unlike acquiring the top-K points from the pool s…
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
Zachary Nado, Neil Band, Mark Collier +23
High-quality estimates of uncertainty and robustness are crucial for numerous real-world applications, especially for deep learning which underlies many deployed ML systems. The ab…
Active Testing: Sample-Efficient Model Evaluation
Jannik Kossen, Sebastian Farquhar, Yarin Gal +1
We introduce a new framework for sample-efficient model evaluation that we call active testing. While approaches like active learning reduce the number of labels needed for model t…
On Statistical Bias In Active Learning: How and When To Fix It
Sebastian Farquhar, Yarin Gal, Tom Rainforth
Active learning is a powerful tool when labelling data is expensive, but it introduces a bias because the training data no longer follows the population distribution. We formalize…