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ORCIDAndreas Kirsch 0002
Person information
- affiliation: University of Oxford, UK
Other persons with the same name
- Andreas Kirsch 0001 — Karlsruhe Institute of Technology (KIT), Department of Mathematics, Germany
- Andreas Kirsch 0003 — University of Bamberg, Germany
- Andreas Kirsch 0004 — independent
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2020 – today
- 2024
[i22]Andreas Kirsch:
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions. CoRR abs/2401.04305 (2024)- [i21]David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade:
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. CoRR abs/2406.10670 (2024) - [i20]Muhammed Razzak, Andreas Kirsch, Yarin Gal:
The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR abs/2406.12011 (2024) - 2023
- [j5]Andreas Kirsch:
Black-Box Batch Active Learning for Regression. Trans. Mach. Learn. Res. 2023 (2023) - [j4]Andreas Kirsch:
Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal:
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c5]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. AISTATS 2023: 7331-7348 - [c4]Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deep Deterministic Uncertainty: A New Simple Baseline. CVPR 2023: 24384-24394 - [i19]Andreas Kirsch:
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning. CoRR abs/2301.09490 (2023) - [i18]Andreas Kirsch:
Black-Box Batch Active Learning for Regression. CoRR abs/2302.08981 (2023) - [i17]Andreas Kirsch:
Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does not. CoRR abs/2303.14753 (2023) - [i16]Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth:
Prediction-Oriented Bayesian Active Learning. CoRR abs/2304.08151 (2023) - 2022
- [j2]Andreas Kirsch, Yarin Gal:
A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. 2022 (2022) - [j1]Andreas Kirsch, Yarin Gal:
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. Trans. Mach. Learn. Res. 2022 (2022) - [c3]Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal:
Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. ICML 2022: 15630-15649 - [i15]Andreas Kirsch, Yarin Gal:
A Note on "Assessing Generalization of SGD via Disagreement". CoRR abs/2202.01851 (2022) - [i14]Andreas Kirsch, Jannik Kossen, Yarin Gal:
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR abs/2205.08766 (2022) - [i13]Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal:
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt. CoRR abs/2206.07137 (2022) - [i12]Dustin Tran, Jeremiah Z. Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan:
Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR abs/2207.07411 (2022) - [i11]Andreas Kirsch, Yarin Gal:
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR abs/2208.00549 (2022) - 2021
- [c2]Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal:
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS 2021: 30465-30478 - [i10]Andreas Kirsch:
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions. CoRR abs/2101.03552 (2021) - [i9]Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal:
Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. CoRR abs/2102.11582 (2021) - [i8]Andreas Kirsch, Tom Rainforth, Yarin Gal:
Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR abs/2106.11719 (2021) - [i7]Andreas Kirsch, Sebastian Farquhar, Yarin Gal:
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR abs/2106.12059 (2021) - [i6]Andreas Kirsch, Yarin Gal:
A Practical & Unified Notation for Information-Theoretic Quantities in ML. CoRR abs/2106.12062 (2021) - [i5]Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal:
Prioritized training on points that are learnable, worth learning, and not yet learned. CoRR abs/2107.02565 (2021) - [i4]Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal:
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR abs/2111.02275 (2021) - 2020
- [i3]Andreas Kirsch
, Clare Lyle, Yarin Gal:
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR abs/2003.12537 (2020)
2010 – 2019
- 2019
- [c1]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS 2019: 7024-7035 - [i2]Andreas Kirsch, Joost van Amersfoort, Yarin Gal:
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR abs/1906.08158 (2019) - 2017
- [i1]Andreas Kirsch:
MDP environments for the OpenAI Gym. CoRR abs/1709.09069 (2017)
Coauthor Index
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