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2020 – today
- 2024
[c11]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. AISTATS 2024: 2386-2394- [c10]Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, David Lindner:
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning. ICLR 2024 - [i18]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 abs/2403.13793 (2024) - [i17]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 abs/2407.04622 (2024) - 2023
- [b1]David Lindner:
Algorithmic Foundations for Safe and Efficient Reinforcement Learning from Human Feedback. ETH Zurich, Zürich, Switzerland, 2023 - [j2]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: 103922 (2023) - [j1]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) - [c9]David Lindner, János Kramár, Sebastian Farquhar, Matthew Rahtz, Tom McGrath, Vladimir Mikulik:
Tracr: Compiled Transformers as a Laboratory for Interpretability. NeurIPS 2023 - [i16]David Lindner, János Kramár, Matthew Rahtz, Thomas McGrath, Vladimir Mikulik:
Tracr: Compiled Transformers as a Laboratory for Interpretability. CoRR abs/2301.05062 (2023) - [i15]David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Learning Safety Constraints from Demonstrations with Unknown Rewards. CoRR abs/2305.16147 (2023) - [i14]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. CoRR abs/2307.15217 (2023) - [i13]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 abs/2308.04332 (2023) - [i12]Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez, David Lindner:
Vision-Language Models are Zero-Shot Reward Models for Reinforcement Learning. CoRR abs/2310.12921 (2023) - 2022
- [c8]David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. ICML 2022: 13505-13527 - [c7]David Lindner, Andreas Krause, Giorgia Ramponi:
Active Exploration for Inverse Reinforcement Learning. NeurIPS 2022 - [i11]Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann:
Scalable Safe Exploration for Global Optimization of Dynamical Systems. CoRR abs/2201.09562 (2022) - [i10]David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause:
Interactively Learning Preference Constraints in Linear Bandits. CoRR abs/2206.05255 (2022) - [i9]David Lindner, Mennatallah El-Assady:
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning. CoRR abs/2206.13316 (2022) - [i8]David Lindner, Andreas Krause, Giorgia Ramponi:
Active Exploration for Inverse Reinforcement Learning. CoRR abs/2207.08645 (2022) - [i7]Javier Rando, Daniel Paleka, David Lindner, Lennart Heim, Florian Tramèr:
Red-Teaming the Stable Diffusion Safety Filter. CoRR abs/2210.04610 (2022) - 2021
- [c6]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. SafeAI@AAAI 2021 - [c5]David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan:
Learning What To Do by Simulating the Past. ICLR 2021 - [c4]David Lindner, Hoda Heidari, Andreas Krause:
Addressing the Long-term Impact of ML Decisions via Policy Regret. IJCAI 2021: 537-544 - [c3]David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause:
Information Directed Reward Learning for Reinforcement Learning. NeurIPS 2021: 3850-3862 - [i6]David Lindner, Kyle Matoba, Alexander Meulemans:
Challenges for Using Impact Regularizers to Avoid Negative Side Effects. CoRR abs/2101.12509 (2021) - [i5]David Lindner, Matteo Turchetta, Sebastian Tschiatschek, Kamil Ciosek, Andreas Krause:
Information Directed Reward Learning for Reinforcement Learning. CoRR abs/2102.12466 (2021) - [i4]David Lindner, Rohin Shah, Pieter Abbeel, Anca D. Dragan:
Learning What To Do by Simulating the Past. CoRR abs/2104.03946 (2021) - [i3]David Lindner, Hoda Heidari, Andreas Krause:
Addressing the Long-term Impact of ML Decisions via Policy Regret. CoRR abs/2106.01325 (2021)
2010 – 2019
- 2019
- [c2]Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh:
Detecting Spiky Corruption in Markov Decision Processes. AISafety@IJCAI 2019 - [c1]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: 1051-1054 - [i2]Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, Nino Antulov-Fantulin:
Sensing Social Media Signals for Cryptocurrency News. CoRR abs/1903.11451 (2019) - [i1]Jason Mancuso, Tomasz Kisielewski, David Lindner, Alok Singh:
Detecting Spiky Corruption in Markov Decision Processes. CoRR abs/1907.00452 (2019)
Coauthor Index
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last updated on 2024-10-07 22:16 CEST by the dblp team
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