294 citations · 412 across the 22 of their papers we have counts for
6 papers · 1 filter
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort +3
Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Ex…
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…
A Practical & Unified Notation for Information-Theoretic Quantities in ML
Andreas Kirsch, Yarin Gal
A practical notation can convey valuable intuitions and concisely express new ideas. Information theory is of importance to machine learning, but the notation for information-theor…
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…
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers
Andreas Kirsch, Tom Rainforth, Yarin Gal
Expanding on MacKay (1992), we argue that conventional model-based methods for active learning - like BALD - have a fundamental shortfall: they fail to directly account for the tes…
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort +2
Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take…