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2020 – today
- 2023
[i28]Lev McKinney, Yawen Duan, David Krueger, Adam Gleave:
On The Fragility of Learned Reward Functions. CoRR abs/2301.03652 (2023)- [i27]Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun, Stéphane Deny:
Blockwise Self-Supervised Learning at Scale. CoRR abs/2302.01647 (2023) - [i26]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 Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstantinos Voudouris, Umang Bhatt, Adrian Weller, David Krueger, Tegan Maharaj:
Harms from Increasingly Agentic Algorithmic Systems. CoRR abs/2302.10329 (2023) - [i25]Xander Davies, Lauro Langosco, David Krueger:
Unifying Grokking and Double Descent. CoRR abs/2303.06173 (2023) - [i24]Micah Carroll, Alan Chan, Henry Ashton, David Krueger:
Characterizing Manipulation from AI Systems. CoRR abs/2303.09387 (2023) - [i23]Shoaib Ahmed Siddiqui, David Krueger, Thomas M. Breuel:
Investigating the Nature of 3D Generalization in Deep Neural Networks. CoRR abs/2304.09358 (2023) - 2022
- [c11]Lauro Langosco di Langosco, Jack Koch, Lee D. Sharkey, Jacob Pfau, David Krueger:
Goal Misgeneralization in Deep Reinforcement Learning. ICML 2022: 12004-12019 - [c10]Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger:
Defining and Characterizing Reward Gaming. NeurIPS 2022 - [i22]Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker:
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics. CoRR abs/2209.10015 (2022) - [i21]Joar Skalse, Nikolaus H. R. Howe, Dmitrii Krasheninnikov, David Krueger:
Defining and Characterizing Reward Hacking. CoRR abs/2209.13085 (2022) - [i20]Adam Ibrahim, Charles Guille-Escuret, Ioannis Mitliagkas, Irina Rish, David Krueger, Pouya Bashivan:
Towards Out-of-Distribution Adversarial Robustness. CoRR abs/2210.03150 (2022) - [i19]Ethan Caballero, Kshitij Gupta, Irina Rish, David Krueger:
Broken Neural Scaling Laws. CoRR abs/2210.14891 (2022) - [i18]Ekdeep Singh Lubana, Eric J. Bigelow, Robert P. Dick, David Krueger, Hidenori Tanaka:
Mechanistic Mode Connectivity. CoRR abs/2211.08422 (2022) - [i17]Alan Clark, Shoaib Ahmed Siddiqui, Robert Kirk, Usman Anwar, Stephen Chung, David Krueger:
Domain Generalization for Robust Model-Based Offline Reinforcement Learning. CoRR abs/2211.14827 (2022) - 2021
- [c9]David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville:
Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML 2021: 5815-5826 - [i16]Shahar Avin, Haydn Belfield, Miles Brundage, Gretchen Krueger, Jasmine Wang, Adrian Weller, Markus Anderljung, Igor Krawczuk, David Krueger, Jonathan Lebensold, Tegan Maharaj, Noa Zilberman:
Filling gaps in trustworthy development of AI. CoRR abs/2112.07773 (2021) - [i15]Enoch Tetteh, Joseph D. Viviano, Yoshua Bengio, David Krueger, Joseph Paul Cohen:
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models. CoRR abs/2112.13734 (2021) - 2020
- [i14]David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Rémi Le Priol, Aaron C. Courville:
Out-of-Distribution Generalization via Risk Extrapolation (REx). CoRR abs/2003.00688 (2020) - [i13]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:
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims. CoRR abs/2004.07213 (2020) - [i12]Andrew Critch, David Krueger:
AI Research Considerations for Human Existential Safety (ARCHES). CoRR abs/2006.04948 (2020) - [i11]David Krueger, Tegan Maharaj, Jan Leike:
Hidden Incentives for Auto-Induced Distributional Shift. CoRR abs/2009.09153 (2020) - [i10]David Krueger, Jan Leike, Owain Evans, John Salvatier:
Active Reinforcement Learning: Observing Rewards at a Cost. CoRR abs/2011.06709 (2020)
2010 – 2019
- 2018
- [c8]Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville:
Neural Autoregressive Flows. ICML 2018: 2083-2092 - [i9]Joel Ruben Antony Moniz, David Krueger:
Nested LSTMs. CoRR abs/1801.10308 (2018) - [i8]Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron C. Courville:
Neural Autoregressive Flows. CoRR abs/1804.00779 (2018) - [i7]Alexandre Lacoste, Boris N. Oreshkin, Wonchang Chung, Thomas Boquet, Negar Rostamzadeh, David Krueger:
Uncertainty in Multitask Transfer Learning. CoRR abs/1806.07528 (2018) - [i6]Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg:
Scalable agent alignment via reward modeling: a research direction. CoRR abs/1811.07871 (2018) - 2017
- [c7]Joel Ruben Antony Moniz, David Krueger:
Nested LSTMs. ACML 2017: 530-544 - [c6]David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville:
Deep Nets Don't Learn via Memorization. ICLR (Workshop) 2017 - [c5]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Aaron C. Courville, Christopher J. Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. ICLR (Poster) 2017 - [c4]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:
A Closer Look at Memorization in Deep Networks. ICML 2017: 233-242 - [i5]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:
A Closer Look at Memorization in Deep Networks. CoRR abs/1706.05394 (2017) - [i4]David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron C. Courville:
Bayesian Hypernetworks. CoRR abs/1710.04759 (2017) - [i3]Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris N. Oreshkin, Wonchang Chung, David Krueger:
Deep Prior. CoRR abs/1712.05016 (2017) - 2016
- [c3]David Krueger, Roland Memisevic:
Regularizing RNNs by Stabilizing Activations. ICLR 2016 - [i2]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR abs/1606.01305 (2016) - 2015
- [c2]Laurent Dinh, David Krueger, Yoshua Bengio:
NICE: Non-linear Independent Components Estimation. ICLR (Workshop) 2015 - [c1]Roland Memisevic, Kishore Reddy Konda, David Krueger:
Zero-bias autoencoders and the benefits of co-adapting features. ICLR (Poster) 2015 - [i1]Philip Bachman, David Krueger, Doina Precup:
Testing Visual Attention in Dynamic Environments. CoRR abs/1510.08949 (2015)
Coauthor Index
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last updated on 2023-05-13 03:58 CEST by the dblp team
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