Sequence Classification using Ensembles of Recurrent Generative Expert Modules. - Marius Hobbhahn; Martin V. Butz; Sarah Fabi; Sebastian Otte (ESANN, 2020)

Laplace Matching for fast Approximate Inference in Generalized Linear Models. - Marius Hobbhahn; Philipp Hennig (CoRR, 2021)

Compute Trends Across Three Eras of Machine Learning. - Jaime Sevilla; Lennart Heim; Anson Ho; Tamay Besiroglu; Marius Hobbhahn; Pablo Villalobos (IJCNN, 2022)

Fast predictive uncertainty for classification with Bayesian deep networks. - Marius Hobbhahn; Agustinus Kristiadi; Philipp Hennig (UAI, 2022)

Machine Learning Model Sizes and the Parameter Gap. - Pablo Villalobos; Jaime Sevilla; Tamay Besiroglu; Lennart Heim; Anson Ho; Marius Hobbhahn (CoRR, 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)

Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure. - Jérémy Scheurer; Mikita Balesni; Marius Hobbhahn (CoRR, 2023)

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; et al. (FAccT, 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 (ICML, 2024)

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs. - Rudolf Laine; Bilal Chughtai; Jan Betley; Kaivalya Hariharan; Mikita Balesni; Jérémy Scheurer; Marius Hobbhahn; Alexander Meinke; Owain Evans (NeurIPS, 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)

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)

Flexible inference in heterogeneous and attributed multilayer networks. - Martina Contisciani; Marius Hobbhahn; Eleanor A. Power; Philipp Hennig; Caterina De Bacco (CoRR, 2024)

Analyzing Probabilistic Methods for Evaluating Agent Capabilities. - Axel Højmark; Govind Pimpale; Arjun Panickssery; Marius Hobbhahn; Jérémy Scheurer (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; et al. (CoRR, 2024)

Frontier Models are Capable of In-context Scheming. - Alexander Meinke; Bronson Schoen; Jérémy Scheurer; Mikita Balesni; Rusheb Shah; Marius Hobbhahn (CoRR, 2024)

Detecting Strategic Deception Using Linear Probes. - Nicholas Goldowsky-Dill; Bilal Chughtai; Stefan Heimersheim; Marius Hobbhahn (CoRR, 2025)

Forecasting Frontier Language Model Agent Capabilities. - Govind Pimpale; Axel Højmark; Jérémy Scheurer; Marius Hobbhahn (CoRR, 2025)

AI Behind Closed Doors: a Primer on The Governance of Internal Deployment. - Charlotte Stix; Matteo Pistillo; Girish Sastry; Marius Hobbhahn; Alejandro Ortega; Mikita Balesni; Annika Hallensleben; Nix Goldowsky-Dill; Lee Sharkey (CoRR, 2025)

Technical Report: Evaluating Goal Drift in Language Model Agents. - Rauno Arike; Elizabeth Donoway; Henning Bartsch; Marius Hobbhahn (CoRR, 2025)

Large Language Models Often Know When They Are Being Evaluated. - Joe Needham; Giles Edkins; Govind Pimpale; Henning Bartsch; Marius Hobbhahn (CoRR, 2025)

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. - Tomek Korbak; Mikita Balesni; Elizabeth Barnes; Yoshua Bengio; Joe Benton; Joseph Bloom; Mark Chen 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)