Andreas Kirsch
Cited by
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Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
A Kirsch, J van Amersfoort, Y Gal
Advances in Neural Information Processing Systems, 7024-7035, 2019
Deterministic neural networks with inductive biases capture epistemic and aleatoric uncertainty
J Mukhoti, A Kirsch, J van Amersfoort, PHS Torr, Y Gal
arXiv preprint arXiv:2102.11582, 2021
Plex: Towards Reliability using Pretrained Large Model Extensions
AC Kirsch, B Lakshminarayanan, CH Hu, D Sculley, D Phan, D Tran, ...
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, 15630-15649, 2022
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
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 34, 30465-30478, 2021
A simple baseline for batch active learning with stochastic acquisition functions
A Kirsch, S Farquhar, Y Gal
arXiv preprint arXiv:2106.12059, 2021
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
A Note on "Assessing Generalization of SGD via Disagreement"
A Kirsch, Y Gal
arXiv preprint arXiv:2202.01851, 2022
Stochastic Batch Acquisition for Deep Active Learning
A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ...
arXiv preprint arXiv:2106.12059, 2021
A practical & unified notation for information-theoretic quantities in ml
A Kirsch, Y Gal
arXiv preprint arXiv:2106.12062, 2021
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
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
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning
A Kirsch
arXiv preprint arXiv:2301.09490, 2023
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
A Kirsch, Y Gal
Transactions on Machine Learning Research, 2022
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
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