Kirsch, Andreas

16 publications

TMLR 2025 (Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models Andreas Kirsch

AISTATS 2025 All Models Are Wrong, Some Are Useful: Model Selection with Limited Labels Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Gürel

ICLR 2025 Turning up the Heat: Min-P Sampling for Creative and Coherent LLM Outputs Nguyen Nhat Minh, Andrew Baker, Clement Neo, Allen G Roush, Andreas Kirsch, Ravid Shwartz-Ziv

NeurIPSW 2024 (Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models Andreas Kirsch

NeurIPS 2024 CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-Training David Brandfonbrener, Hanlin Zhang, Andreas Kirsch, Jonathan Richard Schwarz, Sham Kakade

TMLR 2023 Black-Box Batch Active Learning for Regression Andreas Kirsch

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

TMLR 2023 Does ‘Deep Learning on a Data Diet’ Reproduce? Overall Yes, but GraNd at Initialization Does Not Andreas Kirsch

AISTATS 2023 Prediction-Oriented Bayesian Active Learning Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar, Yarin Gal, Adam Foster, Tom Rainforth

TMLR 2023 Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning Andreas Kirsch, Sebastian Farquhar, Parmida Atighehchian, Andrew Jesson, Frédéric Branchaud-Charron, Yarin Gal

TMLR 2022 A Note on "Assessing Generalization of SGD via Disagreement" Andreas Kirsch, Yarin Gal

ICMLW 2022 Plex: Towards Reliability Using Pretrained Large Model Extensions Dustin Tran, Jeremiah Zhe Liu, Michael W Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda E Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, E. Kelly Buchanan, Kevin Patrick Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan

ICML 2022 Prioritized Training on Points That Are Learnable, Worth Learning, and Not yet Learnt Sören Mindermann, Jan M Brauner, Muhammed T Razzak, Mrinank Sharma, Andreas Kirsch, Winnie Xu, Benedikt Höltgen, Aidan N Gomez, Adrien Morisot, Sebastian Farquhar, Yarin Gal

TMLR 2022 Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities Andreas Kirsch, Yarin Gal

NeurIPS 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

NeurIPS 2019 BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning Andreas Kirsch, Joost van Amersfoort, Yarin Gal