David Lindner

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 31

Top Topics

Eligibility Traces

Decision Process

Reinforcement Learning

Global Optimization

Top Venues

Publications

Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, David Lindner
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning. ICLR (2024)
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
Evaluating Frontier Models for Dangerous Capabilities. CoRR (2024)
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
On scalable oversight with weak LLMs judging strong LLMs. CoRR (2024)
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Learning Safety Constraints from Demonstrations with Unknown Rewards. AISTATS (2024)
David Lindner, János Kramár, Sebastian Farquhar, Matthew Rahtz, Tom McGrath, Vladimir Mikulik
Tracr: Compiled Transformers as a Laboratory for Interpretability. NeurIPS (2023)
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, 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
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. Trans. Mach. Learn. Res. 2023 (2023)
David Lindner, János Kramár, Matthew Rahtz, Thomas McGrath, Vladimir Mikulik
Tracr: Compiled Transformers as a Laboratory for Interpretability. CoRR (2023)
Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, David Lindner
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning. CoRR (2023)
Yannick Metz, David Lindner, Raphaël Baur, Daniel A. Keim, Mennatallah El-Assady
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback. CoRR (2023)
Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann
GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems. Artif. Intell. 320 (2023)
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
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. CoRR (2023)
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Learning Safety Constraints from Demonstrations with Unknown Rewards. CoRR (2023)
David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Interactively Learning Preference Constraints in Linear Bandits. CoRR (2022)
David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
Interactively Learning Preference Constraints in Linear Bandits. ICML (2022)
Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann
Scalable Safe Exploration for Global Optimization of Dynamical Systems. CoRR (2022)
Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr
Red-Teaming the Stable Diffusion Safety Filter. CoRR (2022)
David Lindner, Andreas Krause, Giorgia Ramponi
Active Exploration for Inverse Reinforcement Learning. CoRR (2022)
David Lindner, Mennatallah El-Assady
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning. CoRR (2022)
David Lindner, Andreas Krause, Giorgia Ramponi
Active Exploration for Inverse Reinforcement Learning. NeurIPS (2022)
David Lindner, Kyle Matoba, Alexander Meulemans
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. CoRR (2021)
David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause
Information Directed Reward Learning for Reinforcement Learning. NeurIPS (2021)
David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan
Learning What To Do by Simulating the Past. CoRR (2021)
David Lindner, Kyle Matoba, Alexander Meulemans
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. SafeAI@AAAI (2021)
David Lindner, Hoda Heidari, Andreas Krause
Addressing the Long-term Impact of ML Decisions via Policy Regret. IJCAI (2021)
David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause
Information Directed Reward Learning for Reinforcement Learning. CoRR (2021)
David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan
Learning What To Do by Simulating the Past. ICLR (2021)
David Lindner, Hoda Heidari, Andreas Krause
Addressing the Long-term Impact of ML Decisions via Policy Regret. CoRR (2021)
Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh
Detecting Spiky Corruption in Markov Decision Processes. CoRR (2019)
Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin
Sensing Social Media Signals for Cryptocurrency News. WWW (Companion Volume) (2019)
Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin
Sensing Social Media Signals for Cryptocurrency News. CoRR (2019)
Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh
Detecting Spiky Corruption in Markov Decision Processes. AISafety@IJCAI (2019)