Andreas Kirsch

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 32

Top Topics

Fisher Information

Bayesian Inference

Active Learning

Active Sampling

Top Venues

Trans. Mach. Learn. Res.

Publications

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 (2024)
Muhammed Razzak, Andreas Kirsch, Yarin Gal
The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR (2024)
Andreas Kirsch
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions. CoRR (2024)
Andreas Kirsch
Black-Box Batch Active Learning for Regression. Trans. Mach. Learn. Res. 2023 (2023)
Andreas Kirsch
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning. CoRR (2023)
Andreas Kirsch
Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not. Trans. Mach. Learn. Res. 2023 (2023)
Andreas Kirsch
Black-Box Batch Active Learning for Regression. CoRR (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. AISTATS (2023)
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)
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
Deep Deterministic Uncertainty: A New Simple Baseline. CVPR (2023)
Andreas Kirsch
Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does not. CoRR (2023)
Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
Prediction-Oriented Bayesian Active Learning. CoRR (2023)
Andreas Kirsch, Yarin Gal
A Note on "Assessing Generalization of SGD via Disagreement". CoRR (2022)
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)
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)
Andreas Kirsch, Yarin Gal
A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. 2022 (2022)
Andreas Kirsch, Yarin Gal
Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR (2022)
Dustin Tran, Jeremiah 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 (2022)
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 (2022)
Andreas Kirsch, Jannik Kossen, Yarin Gal
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR (2022)
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)
Andreas Kirsch, Sebastian Farquhar, Yarin Gal
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR (2021)
Andreas Kirsch
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions. CoRR (2021)
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 (2021)
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 (2021)
Andreas Kirsch, Yarin Gal
A Practical & Unified Notation for Information-Theoretic Quantities in ML. CoRR (2021)
Andreas Kirsch, Tom Rainforth, Yarin Gal
Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR (2021)
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 (2021)
Andreas Kirsch, Clare Lyle, Yarin Gal
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR (2020)
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS (2019)
Andreas Kirsch, Joost van Amersfoort, Yarin Gal
BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR (2019)
Andreas Kirsch
MDP environments for the OpenAI Gym. CoRR (2017)