Detecting Spiky Corruption in Markov Decision Processes. - Jason Mancuso; Tomasz Kisielewski; David Lindner; Alok Singh 006 (AISafety@IJCAI, 2019)

Sensing Social Media Signals for Cryptocurrency News. - Johannes Beck; Roberta Huang; David Lindner; Tian Guo 002; Ce Zhang 001; Dirk Helbing; Nino Antulov-Fantulin (WWW (Companion Volume), 2019)

Challenges for Using Impact Regularizers to Avoid Negative Side Effects. - David Lindner; Kyle Matoba; Alexander Meulemans (SafeAI@AAAI, 2021)

Learning What To Do by Simulating the Past. - David Lindner; Rohin Shah; Pieter Abbeel; Anca D. Dragan (ICLR, 2021)

Addressing the Long-term Impact of ML Decisions via Policy Regret. - David Lindner; Hoda Heidari; Andreas Krause 001 (IJCAI, 2021)

Information Directed Reward Learning for Reinforcement Learning. - David Lindner; Matteo Turchetta; Sebastian Tschiatschek; Kamil Ciosek; Andreas Krause 001 (NeurIPS, 2021)

Interactively Learning Preference Constraints in Linear Bandits. - David Lindner; Sebastian Tschiatschek; Katja Hofmann; Andreas Krause 001 (ICML, 2022)

Active Exploration for Inverse Reinforcement Learning. - David Lindner; Andreas Krause 001; Giorgia Ramponi (NeurIPS, 2022)

Scalable Safe Exploration for Global Optimization of Dynamical Systems. - Bhavya Sukhija; Matteo Turchetta; David Lindner; Andreas Krause 001; Sebastian Trimpe; Dominik Baumann (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)

Red-Teaming the Stable Diffusion Safety Filter. - Javier Rando; Daniel Paleka; David Lindner; Lennart Heim; Florian Tramèr (CoRR, 2022)

Tracr: Compiled Transformers as a Laboratory for Interpretability. - David Lindner; János Kramár; Sebastian Farquhar; Matthew Rahtz; Tom McGrath; Vladimir Mikulik (NeurIPS, 2023)

Tracr: Compiled Transformers as a Laboratory for Interpretability. - David Lindner; János Kramár; Matthew Rahtz; Thomas McGrath; Vladimir Mikulik (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 Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Algorithmic Foundations for Safe and Efficient Reinforcement Learning from Human Feedback. - David Lindner (2023)

GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems. - Bhavya Sukhija; Matteo Turchetta; David Lindner; Andreas Krause 001; Sebastian Trimpe; Dominik Baumann (Artif. Intell., 2023)

Learning Safety Constraints from Demonstrations with Unknown Rewards. - David Lindner; Xin Chen; Sebastian Tschiatschek; Katja Hofmann; Andreas Krause 001 (AISTATS, 2024)

Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning. - Juan Rocamonde; Victoriano Montesinos; Elvis Nava; Ethan Perez; David Lindner (ICLR, 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; Rohin Shah (NeurIPS, 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; et al. (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)

Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework. - Yannick Metz; David Lindner; Raphaël Baur; Mennatallah El-Assady (CoRR, 2024)

ViSTa Dataset: Do vision-language models understand sequential tasks? - Evzen Wybitul; Evan Ryan Gunter; Mikhail Seleznyov; David Lindner (CoRR, 2024)

MISR: Measuring Instrumental Self-Reasoning in Frontier Models. - Kai Fronsdal; David Lindner (CoRR, 2024)

MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking. - Sebastian Farquhar; Vikrant Varma; David Lindner; David K. Elson; Caleb Biddulph; Ian Goodfellow; Rohin Shah (CoRR, 2025)

An Approach to Technical AGI Safety and Security. - 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; et al. (CoRR, 2025)

Evaluating Frontier Models for Stealth and Situational Awareness. - Mary Phuong; Roland S. Zimmermann; Ziyue Wang; David Lindner; Victoria Krakovna; Sarah Cogan; Allan Dafoe; Lewis Ho; Rohin Shah (CoRR, 2025)

Large language models can learn and generalize steganographic chain-of-thought under process supervision. - Joey Skaf; Luis Ibañez-Lissen; Robert McCarthy; Connor Watts; Vasil Georgiv; Hannes Whittingham; Lorena González-Manzano; David Lindner; Cameron Tice; Edward James Young; Puria Radmard (CoRR, 2025)

Early Signs of Steganographic Capabilities in Frontier LLMs. - Artur Zolkowski; Kei Nishimura-Gasparian; Robert McCarthy; Roland S. Zimmermann; David Lindner (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)