research
∙
04/17/2023
Prediction-Oriented Bayesian Active Learning
Information-theoretic approaches to active learning have traditionally f...
share
research
∙
03/26/2023
Does `Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not
The paper 'Deep Learning on a Data Diet' by Paul et al. (2021) introduce...
research
∙
02/17/2023
Black-Box Batch Active Learning for Regression
Batch active learning is a popular approach for efficiently training mac...
research
∙
01/23/2023
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning
Active learning is a powerful method for training machine learning model...
research
∙
08/01/2022
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities
The mutual information between predictions and model parameters – also r...
research
∙
06/14/2022
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
Training on web-scale data can take months. But most computation and tim...
research
∙
05/18/2022
Marginal and Joint Cross-Entropies Predictives for Online Bayesian Inference, Active Learning, and Active Sampling
Principled Bayesian deep learning (BDL) does not live up to its potentia...
research
∙
02/03/2022
A Note on "Assessing Generalization of SGD via Disagreement"
Jiang et al. (2021) give empirical evidence that the average test error ...
research
∙
11/03/2021
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Estimating personalized treatment effects from high-dimensional observat...
research
∙
07/06/2021
Prioritized training on points that are learnable, worth learning, and not yet learned
We introduce Goldilocks Selection, a technique for faster model training...
research
∙
06/22/2021
A Practical Unified Notation for Information-Theoretic Quantities in ML
Information theory is of importance to machine learning, but the notatio...
research
∙
06/22/2021
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
In active learning, new labels are commonly acquired in batches. However...
research
∙
06/22/2021
Active Learning under Pool Set Distribution Shift and Noisy Data
Active Learning is essential for more label-efficient deep learning. Bay...
research
∙
02/23/2021
Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty
We show that a single softmax neural net with minimal changes can beat t...
research
∙
01/10/2021
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions
We develop BatchEvaluationBALD, a new acquisition function for deep Baye...
research
∙
03/27/2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning
The information bottleneck (IB) principle offers both a mechanism to exp...
research
∙
06/19/2019
