David Lindner

Publications

2025

  • MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking
    Sebastian Farquhar, Vikrant Varma, David Lindner, David Elson, Caleb Biddulph, Ian Goodfellow, and Rohin Shah
    arXiv:2501.13011, 2025
  • An Approach to Technical AGI Safety and Security
    Rohin Shah, Alex Irpan, Alexander Matt Turner, et al.
    Rohin Shah, Alex Irpan, Alexander Matt Turner, Anna Wang, Arthur Conmy, David Lindner, Jonah Brown-Cohen, Lewis Ho, Neel Nanda, Raluca Ada Popa, Rishub Jain, Rory Greig, Samuel Albanie, Scott Emmons, Sebastian Farquhar, Sébastien Krier, Senthooran Rajamanoharan, Sophie Bridgers, Tobi Ijitoye, Tom Everitt, Victoria Krakovna, Vikrant Varma, Vladimir Mikulik, Zachary Kenton, Dave Orr, Shane Legg, Noah Goodman, Allan Dafoe, Four Flynn, Anca Dragan
    (show all)
    arXiv:2504.01849, 2025

    • 2024

  • MISR: Measuring Instrumental Self-Reasoning in Frontier Models
    Kai Fronsdal and David Lindner
    In Socially Responsible Language Modelling Research (SoLaR) Workshop at NeurIPS, 2024
  • On scalable oversight with weak LLMs judging strong LLMs
    Zachary Kenton, Noah Y. Siegel, János Kramár, Jonah Brown-Cohen, Samuel Albanie, Jannis Bulian, Rishabh Agarwal, David Lindner, Yunhao Tang, Noah D. Goodman, and Rohin Shah
    In Conference on Neural Information Processing Systems (NeurIPS), 2024
  • Towards evaluations-based safety cases for AI scheming
    Mikita Balesni, Marius Hobbhahn, David Lindner, et al.
    Mikita Balesni, Marius Hobbhahn, David Lindner, Alex Meinke, Tomek Korbak, Joshua Clymer, Buck Shlegeris, Jérémy Scheurer, Rusheb Shah, Nicholas Goldowsky-Dill, Dan Braun, Bilal Chughtai, Owain Evans, Daniel Kokotajlo, Lucius Bushnaq
    (show all)
    arXiv:2411.03336, 2024
  • ViSTa Dataset: Do vision-language models understand sequential tasks?
    Evžen Wybitul, Evan Ryan Gunter, Mikhail Seleznyov, and David Lindner
    arXiv:2411.13211, 2024
  • Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
    Yannick Metz, David Lindner, Raphaël Baur, and Mennatallah El-Assady
    arXiv:2411.11761, 2024
  • Learning Safety Constraints from Demonstrations with Unknown Rewards
    David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, and Andreas Krause
    In International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
  • Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning
    Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, and David Lindner
    In International Conference on Learning Representations (ICLR), 2024
  • Evaluating Frontier Models for Dangerous Capabilities
    Mary Phuong, Matthew Aitchison, Elliot Catt, et al.
    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, Gregoire Deletang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca Dragan, Rohin Shah, Allan Dafoe, Toby Shevlane
    (show all)
    arXiv:2403.13793, 2024

    • 2023

  • Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
    Stephen Casper*, Xander Davies*, Claudia Shi, et al.
    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 Segerie, Micah Carroll, Andi Peng, Phillip 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 Bıyık, Anca Dragan, David Krueger, Dorsa Sadigh, Dylan Hadfield-Menell
    (show all)
    In (2023)
  • Tracr: Compiled Transformers as a Laboratory for Interpretability
    David Lindner, János Kramár, Sebastian Farquhar, Matthew Rahtz, Thomas McGrath, and Vladimir Mikulik
    In Conference on Neural Information Processing Systems (NeurIPS), 2023
    Spotlight
  • Algorithmic Foundations for Safe and Efficient Reinforcement Learning from Human Feedback
    David Lindner
    Doctoral Thesis, 2023
  • RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
    Yannick Metz, David Lindner, Raphaël Baur, Daniel Keim, and Mennatallah El-Assady
    In Interactive Learning with Implicit Human Feedback Workshop at ICML, 2023
  • GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
    Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, and Dominik Baumann
    In Artificial Intelligence, 103922 (2023)

    • 2022

  • Red-Teaming the Stable Diffusion Safety Filter
    Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, and Florian Tramèr
    In NeurIPS ML Safety Workshop, 2022
    Best Paper Award
  • Active Exploration for Inverse Reinforcement Learning
    David Lindner, Andreas Krause, and Giorgia Ramponi
    In Conference on Neural Information Processing Systems (NeurIPS), 2022
  • Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning
    David Lindner and Mennatallah El-Assady
    In Communication in Human-AI Interaction Workshop (CHAI) at IJCAI-ECAI, 2022
  • Interactively Learning Preference Constraints in Linear Bandits
    David Lindner, Sebastian Tschiatschek, Katja Hofmann, and Andreas Krause
    In International Conference on Machine Learning (ICML), 2022

    • 2021

  • Information Directed Reward Learning for Reinforcement Learning
    David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, and Andreas Krause
    In Conference on Neural Information Processing Systems (NeurIPS), 2021
  • Addressing the Long-term Impact of ML Decisions via Policy Regret
    David Lindner, Hoda Heidari, and Andreas Krause
    In International Joint Conference on Artificial Intelligence (IJCAI), 2021
  • Learning What To Do by Simulating the Past
    David Lindner, Rohin Shah, Pieter Abbeel, and Anca Dragan
    In International Conference on Learning Representations (ICLR), 2021
  • Challenges for Using Impact Regularizers to Avoid Negative Side Effects
    David Lindner*, Kyle Matoba*, and Alexander Meulemans*
    In SafeAI Workshop at AAAI, 2021

    • 2019

  • Detecting Spiky Corruption in Markov Decision Processes
    Jason Mancuso*, Tomasz Kisielewski*, David Lindner*, and Alok Singh*
    In Workshop on AI Safety at IJCAI, 2019
  • Sensing Social Media Signals for Cryptocurrency News
    Johannes Beck*, Roberta Huang*, David Lindner*, Tian Guo, Ce Zhang, Dirk Helbing, and Nino Antulov-Fantulin
    In International Workshop on Modeling Social Media at WWW, 2019

    • 2018

  • Topological semimetals and insulators in three-dimensional honeycomb materials
    Dennis Wawrzik, David Lindner, Maria Hermanns, and Simon Trebst
    In Phys. Rev. B 98, 115114 (2018)

    • * Equal contribution