Marius Hobbhahn

According to our database, Marius Hobbhahn authored at least 16 papers between 2020 and 2024.

Collaborative distances:

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

2024

Frontier Models are Capable of In-context Scheming.

Alexander Meinke, Bronson Schoen, Jérémy Scheurer, Mikita Balesni, Rusheb Shah, Marius Hobbhahn

CoRR, 2024

Towards evaluations-based safety cases for AI scheming.

Mikita Balesni, Marius Hobbhahn, David Lindner, Alexander Meinke, Tomek Korbak, Joshua Clymer, Buck Shlegeris, Jérémy Scheurer, Charlotte Stix, Rusheb Shah, Nicholas Goldowsky-Dill, Dan Braun, Bilal Chughtai, Owain Evans, Daniel Kokotajlo, Lucius Bushnaq

CoRR, 2024

Analyzing Probabilistic Methods for Evaluating Agent Capabilities.

Axel Højmark, Govind Pimpale, Arjun Panickssery, Marius Hobbhahn, Jérémy Scheurer

CoRR, 2024

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs.

Rudolf Laine, Bilal Chughtai, Jan Betley, Kaivalya Hariharan, Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn, Alexander Meinke, Owain Evans

CoRR, 2024

Flexible inference in heterogeneous and attributed multilayer networks.

Martina Contisciani, Marius Hobbhahn, Eleanor A. Power, Philipp Hennig, Caterina De Bacco

CoRR, 2024

The Local Interaction Basis: Identifying Computationally-Relevant and Sparsely Interacting Features in Neural Networks.

Lucius Bushnaq, Stefan Heimersheim, Nicholas Goldowsky-Dill, Dan Braun, Jake Mendel, Kaarel Hänni, Avery Griffin, Jörn Stöhler, Magdalena Wache, Marius Hobbhahn

CoRR, 2024

Using Degeneracy in the Loss Landscape for Mechanistic Interpretability.

Lucius Bushnaq, Jake Mendel, Stefan Heimersheim, Dan Braun, Nicholas Goldowsky-Dill, Kaarel Hänni, Cindy Wu, Marius Hobbhahn

CoRR, 2024

Position: Will we run out of data? Limits of LLM scaling based on human-generated data.

Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Black-Box Access is Insufficient for Rigorous AI Audits.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas A. Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure.

Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn

CoRR, 2023

2022

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning.

Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, Anson Ho

CoRR, 2022

Machine Learning Model Sizes and the Parameter Gap.

Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Anson Ho, Marius Hobbhahn

CoRR, 2022

Fast predictive uncertainty for classification with Bayesian deep networks.

Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Compute Trends Across Three Eras of Machine Learning.

Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos

Proceedings of the International Joint Conference on Neural Networks, 2022

2021

Laplace Matching for fast Approximate Inference in Generalized Linear Models.

Marius Hobbhahn, Philipp Hennig

CoRR, 2021

2020

Sequence Classification using Ensembles of Recurrent Generative Expert Modules.

Marius Hobbhahn, Martin V. Butz, Sarah Fabi, Sebastian Otte

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020