David Lindner

According to our database, David Lindner authored at least 20 papers between 2019 and 2024.

Collaborative distances:

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

2024

Evaluating Frontier Models for Dangerous Capabilities.

Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, Heidi Howard, Tom Lieberum, Ramana Kumar, Maria Abi Raad, Albert Webson, Lewis Ho, Sharon Lin, Sebastian Farquhar, Marcus Hutter, Grégoire Delétang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca D. Dragan, Rohin Shah, Allan Dafoe, Toby Shevlane

CoRR, 2024

Learning Safety Constraints from Demonstrations with Unknown Rewards.

David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems.

Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann

Artif. Intell., July, 2023

Algorithmic Foundations for Safe and Efficient Reinforcement Learning from Human Feedback.

David Lindner

PhD thesis, 2023

Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning.

Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, David Lindner

CoRR, 2023

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback.

Yannick Metz, David Lindner, Raphaël Baur, Daniel A. Keim, Mennatallah El-Assady

CoRR, 2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Ségerie, Micah Carroll, Andi Peng, Phillip J. K. Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranjan, Max Nadeau, Eric J. Michaud, Jacob Pfau, Dmitrii Krasheninnikov, Xin Chen, Lauro Langosco, Peter Hase, Erdem Biyik, Anca D. Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell

CoRR, 2023

Tracr: Compiled Transformers as a Laboratory for Interpretability.

David Lindner, János Kramár, Matthew Rahtz, Thomas McGrath, Vladimir Mikulik

CoRR, 2023

Tracr: Compiled Transformers as a Laboratory for Interpretability.

David Lindner, János Kramár, Sebastian Farquhar, Matthew Rahtz, Tom McGrath, Vladimir Mikulik

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

2022

Red-Teaming the Stable Diffusion Safety Filter.

Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr

CoRR, 2022

Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning.

David Lindner, Mennatallah El-Assady

CoRR, 2022

Scalable Safe Exploration for Global Optimization of Dynamical Systems.

Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann

CoRR, 2022

Active Exploration for Inverse Reinforcement Learning.

David Lindner, Andreas Krause, Giorgia Ramponi

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

Interactively Learning Preference Constraints in Linear Bandits.

David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

Proceedings of the International Conference on Machine Learning, 2022

2021

Information Directed Reward Learning for Reinforcement Learning.

David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause

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

Addressing the Long-term Impact of ML Decisions via Policy Regret.

David Lindner, Hoda Heidari, Andreas Krause

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Learning What To Do by Simulating the Past.

David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan

Proceedings of the 9th International Conference on Learning Representations, 2021

Challenges for Using Impact Regularizers to Avoid Negative Side Effects.

David Lindner, Kyle Matoba, Alexander Meulemans

Proceedings of the Workshop on Artificial Intelligence Safety 2021 (SafeAI 2021) co-located with the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), 2021

2019

Sensing Social Media Signals for Cryptocurrency News.

Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin

Proceedings of the Companion of The 2019 World Wide Web Conference, 2019

Detecting Spiky Corruption in Markov Decision Processes.

Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh

Proceedings of the Workshop on Artificial Intelligence Safety 2019 co-located with the 28th International Joint Conference on Artificial Intelligence, 2019