Andreas Kirsch
Cited by
Cited by
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
A Kirsch, J van Amersfoort, Y Gal
Advances in Neural Information Processing Systems (NeurIPS), 7024-7035, 2019
Deep Deterministic Uncertainty: A New Simple Baseline
J Mukhoti, A Kirsch, J van Amersfoort, PHS Torr, Y Gal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Plex: towards reliability using pretrained large model extensions (2022)
D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ...
URL https://arxiv. org/abs/2207.07411, 0
Prioritized training on points that are learnable, worth learning, and not yet learnt
S Mindermann, JM Brauner, MT Razzak, M Sharma, A Kirsch, W Xu, ...
International Conference on Machine Learning (ICML), 15630-15649, 2022
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ...
Transactions on Machine Learning Research (TMLR), 0
Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data
A Jesson, P Tigas, J van Amersfoort, A Kirsch, U Shalit, Y Gal
Advances in Neural Information Processing Systems (NeurIPS) 34, 30465-30478, 2021
Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning
A Kirsch, C Lyle, Y Gal
Workshop Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine …, 2020
A Note on "Assessing Generalization of SGD via Disagreement"
A Kirsch, Y Gal
Transactions on Machine Learning Research (TMLR), 2022
Test distribution-aware active learning: A principled approach against distribution shift and outliers
A Kirsch, T Rainforth, Y Gal
arXiv preprint arXiv:2106.11719, 2021
Prediction-Oriented Bayesian Active Learning
FB Smith, A Kirsch, S Farquhar, Y Gal, A Foster, T Rainforth
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023
A Practical & Unified Notation for Information-Theoretic Quantities in ML
A Kirsch, Y Gal
arXiv preprint arXiv:2106.12062, 2021
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
A Kirsch, Y Gal
Transactions on Machine Learning Research (TMLR), 2022
Black-Box Batch Active Learning for Regression
A Kirsch
Transactions on Machine Learning Research, 2023
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions
A Kirsch
arXiv preprint arXiv:2101.03552, 2021
MDP environments for the OpenAI Gym
A Kirsch
arXiv preprint arXiv:1709.09069, 2017
Does Deep Learning on a Data Diet reproduce? Overall yes, but GraNd at Initialization does not
A Kirsch
Transactions on Machine Learning Research, 2023
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning
A Kirsch
arXiv preprint arXiv:2301.09490, 2023
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling
A Kirsch, J Kossen, Y Gal
arXiv preprint arXiv:2205.08766, 2022
Proseminar: Perlen der Informatik II Aussagenlogik–Korrektheit und Vollständigkeit des natürlichen Schließens
A Kirsch
The system can't perform the operation now. Try again later.
Articles 1–19