294 citations · 412 across the 22 of their papers we have counts for
22 papers
Large Language Model Selection with Limited Annotations
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. T…
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
Minh Nhat Nguyen, Andrew Baker, Clement Neo +3
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p…
The Benefits and Risks of Transductive Approaches for AI Fairness
Muhammed Razzak, Andreas Kirsch, Yarin Gal
Recently, transductive learning methods, which leverage holdout sets during training, have gained popularity for their potential to improve speed, accuracy, and fairness in machine…
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…
Prediction-Oriented Bayesian Active Learning
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar +3
Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BA…
Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does not
Andreas Kirsch
The paper 'Deep Learning on a Data Diet' by Paul et al. (2021) introduces two innovative metrics for pruning datasets during the training of neural networks. While we are able to r…