Jannik Kossen

According to our database, Jannik Kossen authored at least 20 papers between 2020 and 2026.

Collaborative distances:

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

2026

Towards a Neural Debugger for Python.

Maximilian Beck, Jonas Gehring, Jannik Kossen, Gabriel Synnaeve

CoRR, March, 2026

2025

CWM: An Open-Weights LLM for Research on Code Generation with World Models.

FAIR CodeGen team, Jade Copet, Quentin Carbonneaux, Gal Cohen, Jonas Gehring, Jacob Kahn, Jannik Kossen, Felix Kreuk, Emily McMilin, Michel Meyer, Yuxiang Wei, David Zhang, Kunhao Zheng, Jordi Armengol-Estapé, Pedram Bashiri, Maximilian Beck, Pierre Chambon, Abhishek Charnalia, Chris Cummins, Juliette Decugis, Zacharias V. Fisches, François Fleuret, Fabian Gloeckle, Alex Gu, Michael Hassid, Daniel Haziza, Badr Youbi Idrissi, Christian Keller, Rahul Kindi, Hugh Leather, Gallil Maimon, Aram H. Markosyan, Francisco Massa, Pierre-Emmanuel Mazaré, Vegard Mella, Naila Murray, Keyur Muzumdar, Peter W. O'Hearn, Matteo Pagliardini, Dmitrii Pedchenko, Tal Remez, Volker Seeker, Marco Selvi, Oren Sultan, Sida Wang, Luca Wehrstedt, Ori Yoran, Lingming Zhang, Taco Cohen, Yossi Adi, Gabriel Synnaeve

CoRR, October, 2025

Reducing Large Language Model Safety Risks in Women's Health using Semantic Entropy.

Jahan C. Penny-Dimri, Magdalena Bachmann, William R. Cooke, Sam Mathewlynn, Samuel Dockree, John Tolladay, Jannik Kossen, Lin Li, Yarin Gal, Gabriel Davis Jones

CoRR, March, 2025

Scaling Up Active Testing to Large Language Models.

Gabrielle Berrada, Jannik Kossen, Freddie Bickford Smith, Muhammed Razzak, Yarin Gal, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Rethinking Aleatoric and Epistemic Uncertainty.

Freddie Bickford Smith, Jannik Kossen, Eleanor Trollope, Mark van der Wilk, Adam Foster, Tom Rainforth

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation.

Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, Yarin Gal

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2025, 2025

2024

Detecting hallucinations in large language models using semantic entropy.

Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, Yarin Gal

Nat., June, 2024

Fine-Tuning Large Language Models to Appropriately Abstain with Semantic Entropy.

Benedict Aaron Tjandra, Muhammed Razzak, Jannik Kossen, Kunal Handa, Yarin Gal

CoRR, 2024

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs.

Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth A. Malik, Yarin Gal

CoRR, 2024

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities.

Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Estimating the Hallucination Rate of Generative AI.

Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

In-Context Learning Learns Label Relationships but Is Not Conventional Learning.

Jannik Kossen, Yarin Gal, Tom Rainforth

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task.

Jannik Kossen, Catalina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave

Trans. Mach. Learn. Res., 2023

In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning.

Jannik Kossen, Tom Rainforth, Yarin Gal

CoRR, 2023

Three Towers: Flexible Contrastive Learning with Pretrained Image Models.

Jannik Kossen, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

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

Andreas Kirsch, Jannik Kossen, Yarin Gal

CoRR, 2022

Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation.

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Thomas Rainforth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning.

Jannik Kossen, Neil Band, Clare Lyle, Aidan N. Gomez, Thomas Rainforth, Yarin Gal

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

Active Testing: Sample-Efficient Model Evaluation.

Jannik Kossen, Sebastian Farquhar, Yarin Gal, Tom Rainforth

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Structured Object-Aware Physics Prediction for Video Modeling and Planning.

Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

Proceedings of the 8th International Conference on Learning Representations, 2020