Andreas Kirsch

According to our database, Andreas Kirsch authored at least 25 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Benefits and Risks of Transductive Approaches for AI Fairness.

Muhammed Razzak, Andreas Kirsch, Yarin Gal

CoRR, 2024

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training.

David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade

CoRR, 2024

Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions.

Andreas Kirsch

CoRR, 2024

2023

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning.

Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal

Trans. Mach. Learn. Res., 2023

Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not.

Andreas Kirsch

Trans. Mach. Learn. Res., 2023

Black-Box Batch Active Learning for Regression.

Andreas Kirsch

Trans. Mach. Learn. Res., 2023

Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning.

Andreas Kirsch

CoRR, 2023

Deep Deterministic Uncertainty: A New Simple Baseline.

Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Prediction-Oriented Bayesian Active Learning.

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities.

Andreas Kirsch, Yarin Gal

Trans. Mach. Learn. Res., 2022

A Note on "Assessing Generalization of SGD via Disagreement".

Andreas Kirsch, Yarin Gal

Trans. Mach. Learn. Res., 2022

Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities.

Andreas Kirsch, Yarin Gal

CoRR, 2022

Plex: Towards Reliability using Pretrained Large Model Extensions.

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

CoRR, 2022

Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling.

Andreas Kirsch, Jannik Kossen, Yarin Gal

CoRR, 2022

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt.

Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal

Proceedings of the International Conference on Machine Learning, 2022

2021

Prioritized training on points that are learnable, worth learning, and not yet learned.

Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal

CoRR, 2021

A Practical & Unified Notation for Information-Theoretic Quantities in ML.

Andreas Kirsch, Yarin Gal

CoRR, 2021

A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions.

Andreas Kirsch, Sebastian Farquhar, Yarin Gal

CoRR, 2021

Active Learning under Pool Set Distribution Shift and Noisy Data.

Andreas Kirsch, Tom Rainforth, Yarin Gal

CoRR, 2021

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty.

Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

CoRR, 2021

PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions.

Andreas Kirsch

CoRR, 2021

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning.

Andreas Kirsch, Clare Lyle, Yarin Gal

CoRR, 2020

2019

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning.

Andreas Kirsch, Joost van Amersfoort, Yarin Gal

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

MDP environments for the OpenAI Gym.

Andreas Kirsch

CoRR, 2017