Andreas Kirsch

34 papers A* 5A 1Journal 28

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models. Trans. Mach. Learn. Res. Andreas Kirsch
2024	J	`jnl`	Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions. CoRR Andreas Kirsch
2024	A*	`conf`	CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. NeurIPS David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade
2024	J	`jnl`	CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training. CoRR David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham M. Kakade
2024	J	`jnl`	The Benefits and Risks of Transductive Approaches for AI Fairness. CoRR Muhammed Razzak, Andreas Kirsch, Yarin Gal
2023	J	`jnl`	Black-Box Batch Active Learning for Regression. CoRR Andreas Kirsch
2023	J	`jnl`	Black-Box Batch Active Learning for Regression. Trans. Mach. Learn. Res. Andreas Kirsch
2023	A*	`conf`	Deep Deterministic Uncertainty: A New Simple Baseline. CVPR Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
2023	J	`jnl`	Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does not. CoRR Andreas Kirsch
2023	J	`jnl`	Does 'Deep Learning on a Data Diet' reproduce? Overall yes, but GraNd at Initialization does not. Trans. Mach. Learn. Res. Andreas Kirsch
2023	A	`conf`	Prediction-Oriented Bayesian Active Learning. AISTATS Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Prediction-Oriented Bayesian Active Learning. CoRR Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth
2023	J	`jnl`	Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning. CoRR Andreas Kirsch
2023	J	`jnl`	Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning. Trans. Mach. Learn. Res. Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal
2022	J	`jnl`	A Note on "Assessing Generalization of SGD via Disagreement". CoRR Andreas Kirsch, Yarin Gal
2022	J	`jnl`	A Note on "Assessing Generalization of SGD via Disagreement". Trans. Mach. Learn. Res. Andreas Kirsch, Yarin Gal
2022	J	`jnl`	Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling. CoRR Andreas Kirsch, Jannik Kossen, Yarin Gal
2022	J	`jnl`	Plex: Towards Reliability using Pretrained Large Model Extensions. CoRR 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
2022	J	`jnl`	Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt. CoRR Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
2022	A*	`conf`	Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt. ICML Sören Mindermann, Jan Markus Brauner, Muhammed Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N. Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal
2022	J	`jnl`	Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities. Trans. Mach. Learn. Res. Andreas Kirsch, Yarin Gal
2022	J	`jnl`	Unifying Approaches in Data Subset Selection via Fisher Information and Information-Theoretic Quantities. CoRR Andreas Kirsch, Yarin Gal
2021	J	`jnl`	A Practical & Unified Notation for Information-Theoretic Quantities in ML. CoRR Andreas Kirsch, Yarin Gal
2021	J	`jnl`	A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions. CoRR Andreas Kirsch, Sebastian Farquhar, Yarin Gal
2021	J	`jnl`	Active Learning under Pool Set Distribution Shift and Noisy Data. CoRR Andreas Kirsch, Tom Rainforth, Yarin Gal
2021	A*	`conf`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. NeurIPS Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data. CoRR Andrew Jesson, Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal
2021	J	`jnl`	Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty. CoRR Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal
2021	J	`jnl`	PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions. CoRR Andreas Kirsch
2021	J	`jnl`	Prioritized training on points that are learnable, worth learning, and not yet learned. CoRR Sören Mindermann, Muhammed Razzak, Winnie Xu, Andreas Kirsch, Mrinank Sharma, Adrien Morisot, Aidan N. Gomez, Sebastian Farquhar, Jan Markus Brauner, Yarin Gal
2020	J	`jnl`	Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning. CoRR Andreas Kirsch, Clare Lyle, Yarin Gal
2019	A*	`conf`	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. NeurIPS Andreas Kirsch, Joost van Amersfoort, Yarin Gal
2019	J	`jnl`	BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning. CoRR Andreas Kirsch, Joost van Amersfoort, Yarin Gal
2017	J	`jnl`	MDP environments for the OpenAI Gym. CoRR Andreas Kirsch