MDP environments for the OpenAI Gym. - Andreas Kirsch 002 (CoRR, 2017)

BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. - Andreas Kirsch 002; Joost van Amersfoort; Yarin Gal (NeurIPS, 2019)

Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. - Andreas Kirsch 002; Clare Lyle; Yarin Gal (CoRR, 2020)

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. - Andrew Jesson; Panagiotis Tigas; Joost van Amersfoort; Andreas Kirsch 002; Uri Shalit; Yarin Gal (NeurIPS, 2021)

PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions. - Andreas Kirsch 002 (CoRR, 2021)

Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. - Jishnu Mukhoti; Andreas Kirsch 002; Joost van Amersfoort; Philip H. S. Torr; Yarin Gal (CoRR, 2021)

Active Learning under Pool Set Distribution Shift and Noisy Data. - Andreas Kirsch 002; Tom Rainforth; Yarin Gal (CoRR, 2021)

A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. - Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal (CoRR, 2021)

A Practical & Unified Notation for Information-Theoretic Quantities in ML. - Andreas Kirsch 002; Yarin Gal (CoRR, 2021)

Prioritized training on points that are learnable, worth learning, and not yet learned. - Sören Mindermann; Muhammed Razzak; Winnie Xu; Andreas Kirsch 002; Mrinank Sharma; Adrien Morisot; Aidan N. Gomez; Sebastian Farquhar; Jan Markus Brauner; Yarin Gal (CoRR, 2021)

Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. - Sören Mindermann; Jan Markus Brauner; Muhammed Razzak; Mrinank Sharma; Andreas Kirsch 002; Winnie Xu; Benedikt Höltgen; Aidan N. Gomez; Adrien Morisot; Sebastian Farquhar; Yarin Gal (ICML, 2022)

Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. - Andreas Kirsch 002; Jannik Kossen; 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 006; Kehang Han; Zi Wang; Zelda Mariet; Huiyi Hu; Neil Band; Tim G. J. Rudner; et al. (CoRR, 2022)

Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. - Andreas Kirsch 002; Yarin Gal (CoRR, 2022)

A Note on "Assessing Generalization of SGD via Disagreement". - Andreas Kirsch 002; Yarin Gal (Trans. Mach. Learn. Res., 2022)

Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. - Andreas Kirsch 002; Yarin Gal (Trans. Mach. Learn. Res., 2022)

Prediction-Oriented Bayesian Active Learning. - Freddie Bickford Smith; Andreas Kirsch 002; Sebastian Farquhar; Yarin Gal; Adam Foster 001; Tom Rainforth (AISTATS, 2023)

Deep Deterministic Uncertainty: A New Simple Baseline. - Jishnu Mukhoti; Andreas Kirsch 002; Joost van Amersfoort; Philip H. S. Torr; Yarin Gal (CVPR, 2023)

Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning. - Andreas Kirsch 002 (CoRR, 2023)

Black-Box Batch Active Learning for Regression. - Andreas Kirsch 002 (Trans. Mach. Learn. Res., 2023)

Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not. - Andreas Kirsch 002 (Trans. Mach. Learn. Res., 2023)

Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. - Andreas Kirsch 002; Sebastian Farquhar; Parmida Atighehchian; Andrew Jesson; Frédéric Branchaud-Charron; Yarin Gal (Trans. Mach. Learn. Res., 2023)

CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. - David Brandfonbrener; Hanlin Zhang 002; Andreas Kirsch 002; Jonathan Richard Schwarz; Sham M. Kakade (NeurIPS, 2024)

Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions. - Andreas Kirsch 002 (CoRR, 2024)

The Benefits and Risks of Transductive Approaches for AI Fairness. - Muhammed Razzak; Andreas Kirsch 002; Yarin Gal (CoRR, 2024)

(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models. - Andreas Kirsch 002 (Trans. Mach. Learn. Res., 2025)