David Krueger

According to our database, David Krueger authored at least 45 papers between 2015 and 2024.

Collaborative distances:

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

2024

Safety Cases: How to Justify the Safety of Advanced AI Systems.

Joshua Clymer, Nick Gabrieli, David Krueger, Thomas Larsen

CoRR, 2024

A Generative Model of Symmetry Transformations.

James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato

CoRR, 2024

Black-Box Access is Insufficient for Rigorous AI Audits.

Stephen Casper, Carson Ezell, Charlotte Siegmann, Noam Kolt, Taylor Lynn Curtis, Benjamin Bucknall, Andreas Alexander Haupt, Kevin Wei, Jérémy Scheurer, Marius Hobbhahn, Lee Sharkey, Satyapriya Krishna, Marvin Von Hagen, Silas Alberti, Alan Chan, Qinyi Sun, Michael Gerovitch, David Bau, Max Tegmark, David Krueger, Dylan Hadfield-Menell

CoRR, 2024

Visibility into AI Agents.

Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung

CoRR, 2024

2023

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models.

Alan Chan, Ben Bucknall, Herbie Bradley, David Krueger

CoRR, 2023

Managing AI Risks in an Era of Rapid Progress.

Yoshua Bengio, Geoffrey E. Hinton, Andrew Yao, Dawn Song, Pieter Abbeel, Yuval Noah Harari, Ya-Qin Zhang, Lan Xue, Shai Shalev-Shwartz, Gillian K. Hadfield, Jeff Clune, Tegan Maharaj, Frank Hutter, Atilim Günes Baydin, Sheila A. McIlraith, Qiqi Gao, Ashwin Acharya, David Krueger, Anca D. Dragan, Philip H. S. Torr, Stuart Russell, Daniel Kahneman, Jan Brauner, Sören Mindermann

CoRR, 2023

Meta- (out-of-context) learning in neural networks.

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec, David Krueger

CoRR, 2023

Reward Model Ensembles Help Mitigate Overoptimization.

Thomas Coste, Usman Anwar, Robert Kirk, David Krueger

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

Investigating the Nature of 3D Generalization in Deep Neural Networks.

Shoaib Ahmed Siddiqui, David Krueger, Thomas M. Breuel

CoRR, 2023

Unifying Grokking and Double Descent.

Xander Davies, Lauro Langosco, David Krueger

CoRR, 2023

Blockwise Self-Supervised Learning at Scale.

Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun, Stéphane Deny

CoRR, 2023

On The Fragility of Learned Reward Functions.

Lev McKinney, Yawen Duan, David Krueger, Adam Gleave

CoRR, 2023

Thinker: Learning to Plan and Act.

Stephen Chung, Ivan Anokhin, David Krueger

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

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics.

Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Broken Neural Scaling Laws.

Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Harms from Increasingly Agentic Algorithmic Systems.

Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine M. Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, Tegan Maharaj

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

Characterizing Manipulation from AI Systems.

Micah Carroll, Alan Chan, Henry Ashton, David Krueger

Proceedings of the 3rd ACM Conference on Equity and Access in Algorithms, 2023

2022

Domain Generalization for Robust Model-Based Offline Reinforcement Learning.

Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk, Usman Anwar, Stephen Chung, David Krueger

CoRR, 2022

Towards Out-of-Distribution Adversarial Robustness.

Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan

CoRR, 2022

Defining and Characterizing Reward Hacking.

Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger

CoRR, 2022

Defining and Characterizing Reward Gaming.

Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger

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

Goal Misgeneralization in Deep Reinforcement Learning.

Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger

Proceedings of the International Conference on Machine Learning, 2022

2021

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models.

Enoch Tetteh, Joseph D. Viviano, Yoshua Bengio, David Krueger, Joseph Paul Cohen

CoRR, 2021

Filling gaps in trustworthy development of AI.

Shahar Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David Krueger, Jonathan Lebensold, Tegan Maharaj, Noa Zilberman

CoRR, 2021

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Active Reinforcement Learning: Observing Rewards at a Cost.

David Krueger, Jan Leike, Owain Evans, John Salvatier

CoRR, 2020

Hidden Incentives for Auto-Induced Distributional Shift.

David Krueger, Tegan Maharaj, Jan Leike

CoRR, 2020

AI Research Considerations for Human Existential Safety (ARCHES).

Andrew Critch, David Krueger

CoRR, 2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims.

Miles Brundage, Shahar Avin, Jasmine Wang, Haydn Belfield, Gretchen Krueger, Gillian K. Hadfield, Heidy Khlaaf, Jingying Yang, Helen Toner, Ruth Fong, Tegan Maharaj, Pang Wei Koh, Sara Hooker, Jade Leung, Andrew Trask, Emma Bluemke, Jonathan Lebensold, Cullen O'Keefe, Mark Koren, Théo Ryffel, J. B. Rubinovitz, Tamay Besiroglu, Federica Carugati, Jack Clark, Peter Eckersley, Sarah de Haas, Maritza Johnson, Ben Laurie, Alex Ingerman, Igor Krawczuk, Amanda Askell, Rosario Cammarota, Andrew Lohn, David Krueger, Charlotte Stix, Peter Henderson, Logan Graham, Carina Prunkl, Bianca Martin, Elizabeth Seger, Noa Zilberman, Seán Ó hÉigeartaigh, Frens Kroeger, Girish Sastry, Rebecca Kagan, Adrian Weller, Brian Tse, Elizabeth Barnes, Allan Dafoe, Paul Scharre, Ariel Herbert-Voss, Martijn Rasser, Shagun Sodhani, Carrick Flynn, Thomas Krendl Gilbert, Lisa Dyer, Saif Khan, Yoshua Bengio, Markus Anderljung

CoRR, 2020

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville

CoRR, 2020

2018

Scalable agent alignment via reward modeling: a research direction.

Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg

CoRR, 2018

Uncertainty in Multitask Transfer Learning.

Alexandre Lacoste, Boris N. Oreshkin, Wonchang Chung, Thomas Boquet, Negar Rostamzadeh, David Krueger

CoRR, 2018

Neural Autoregressive Flows.

Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Deep Prior.

Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris N. Oreshkin, Wonchang Chung, David Krueger

CoRR, 2017

Bayesian Hypernetworks.

David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron C. Courville

CoRR, 2017

A Closer Look at Memorization in Deep Networks.

Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien

Proceedings of the 34th International Conference on Machine Learning, 2017

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal

Proceedings of the 5th International Conference on Learning Representations, 2017

Deep Nets Don't Learn via Memorization.

David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville

Proceedings of the 5th International Conference on Learning Representations, 2017

Nested LSTMs.

Joel Ruben Antony Moniz, David Krueger

Proceedings of The 9th Asian Conference on Machine Learning, 2017

2016

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations.

David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal

CoRR, 2016

Regularizing RNNs by Stabilizing Activations.

David Krueger, Roland Memisevic

Proceedings of the 4th International Conference on Learning Representations, 2016

2015

Zero-bias autoencoders and the benefits of co-adapting features.

Roland Memisevic, Kishore Reddy Konda, David Krueger

Proceedings of the 3rd International Conference on Learning Representations, 2015

NICE: Non-linear Independent Components Estimation.

Laurent Dinh, David Krueger, Yoshua Bengio

Proceedings of the 3rd International Conference on Learning Representations, 2015

Testing Visual Attention in Dynamic Environments.

Philip Bachman, David Krueger, Doina Precup

CoRR, 2015