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ORCIDMarius Hobbhahn
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2020 – today
- 2024
[c5]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:
Black-Box Access is Insufficient for Rigorous AI Audits. FAccT 2024: 2254-2272- [c4]Pablo Villalobos, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Marius Hobbhahn:
Position: Will we run out of data? Limits of LLM scaling based on human-generated data. ICML 2024 - [i12]Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander 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:
Black-Box Access is Insufficient for Rigorous AI Audits. CoRR abs/2401.14446 (2024) - [i11]Lucius Bushnaq, Jake Mendel, Stefan Heimersheim, Dan Braun, Nicholas Goldowsky-Dill, Kaarel Hänni, Cindy Wu, Marius Hobbhahn:
Using Degeneracy in the Loss Landscape for Mechanistic Interpretability. CoRR abs/2405.10927 (2024) - [i10]Lucius Bushnaq, Stefan Heimersheim, Nicholas Goldowsky-Dill, Dan Braun, Jake Mendel, Kaarel Hänni, Avery Griffin, Jörn Stöhler, Magdalena Wache, Marius Hobbhahn:
The Local Interaction Basis: Identifying Computationally-Relevant and Sparsely Interacting Features in Neural Networks. CoRR abs/2405.10928 (2024) - [i9]Martina Contisciani, Marius Hobbhahn, Eleanor A. Power, Philipp Hennig, Caterina De Bacco:
Flexible inference in heterogeneous and attributed multilayer networks. CoRR abs/2405.20918 (2024) - [i8]Rudolf Laine, Bilal Chughtai, Jan Betley, Kaivalya Hariharan, Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn, Alexander Meinke, Owain Evans:
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs. CoRR abs/2407.04694 (2024) - [i7]Axel Højmark, Govind Pimpale, Arjun Panickssery, Marius Hobbhahn, Jérémy Scheurer:
Analyzing Probabilistic Methods for Evaluating Agent Capabilities. CoRR abs/2409.16125 (2024) - 2023
- [i6]Jérémy Scheurer, Mikita Balesni, Marius Hobbhahn:
Technical Report: Large Language Models can Strategically Deceive their Users when Put Under Pressure. CoRR abs/2311.07590 (2023) - 2022
- [c3]Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos:
Compute Trends Across Three Eras of Machine Learning. IJCNN 2022: 1-8 - [c2]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast predictive uncertainty for classification with Bayesian deep networks. UAI 2022: 822-832 - [i5]Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, Pablo Villalobos:
Compute Trends Across Three Eras of Machine Learning. CoRR abs/2202.05924 (2022) - [i4]Pablo Villalobos, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, Anson Ho, Marius Hobbhahn:
Machine Learning Model Sizes and the Parameter Gap. CoRR abs/2207.02852 (2022) - [i3]Pablo Villalobos, Jaime Sevilla, Lennart Heim, Tamay Besiroglu, Marius Hobbhahn, Anson Ho:
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning. CoRR abs/2211.04325 (2022) - 2021
- [i2]Marius Hobbhahn, Philipp Hennig:
Laplace Matching for fast Approximate Inference in Generalized Linear Models. CoRR abs/2105.03109 (2021) - 2020
- [c1]Marius Hobbhahn, Martin V. Butz, Sarah Fabi, Sebastian Otte:
Sequence Classification using Ensembles of Recurrent Generative Expert Modules. ESANN 2020: 333-338 - [i1]Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig:
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR abs/2003.01227 (2020)
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last updated on 2024-10-17 21:30 CEST by the dblp team
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