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Nitarshan Rajkumar
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2020 – today
- 2024
- [c7]Alan Chan
, Carson Ezell
Visibility into AI Agents. FAccT 2024: 958-973 - [i10]Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung:
Visibility into AI Agents. CoRR abs/2401.13138 (2024) - [i9]Alan Chan, Noam Kolt, Peter Wills, Usman Anwar, Christian Schröder de Witt, Nitarshan Rajkumar, Lewis Hammond, David Krueger, Lennart Heim, Markus Anderljung:
IDs for AI Systems. CoRR abs/2406.12137 (2024) - [i8]Anka Reuel, Ben Bucknall, Stephen Casper, Tim Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Alexandra Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager:
Open Problems in Technical AI Governance. CoRR abs/2407.14981 (2024) - 2023
- [c6]Alan Chan
Reclaiming the Digital Commons: A Public Data Trust for Training Data. AIES 2023: 855-868 - [c5]Alan Chan
Harms from Increasingly Agentic Algorithmic Systems. FAccT 2023: 651-666 - [c4]Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker:
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics. ICLR 2023 - [i7]Alan Chan, Rebecca Salganik, Alva Markelius
Harms from Increasingly Agentic Algorithmic Systems. CoRR abs/2302.10329 (2023) - [i6]Alan Chan, Herbie Bradley, Nitarshan Rajkumar:
Reclaiming the Digital Commons: A Public Data Trust for Training Data. CoRR abs/2303.09001 (2023) - 2022
- [c3]Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon:
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. NeurIPS 2022 - [i5]Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon:
Myriad: a real-world testbed to bridge trajectory optimization and deep learning. CoRR abs/2202.10600 (2022) - [i4]Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau:
Evaluating the Text-to-SQL Capabilities of Large Language Models. CoRR abs/2204.00498 (2022) - [i3]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) - 2021
- [c2]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS 2021: 12686-12699 - [i2]Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville:
Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR abs/2106.04799 (2021) - 2020
- [c1]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In search of robust measures of generalization. NeurIPS 2020 - [i1]Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy:
In Search of Robust Measures of Generalization. CoRR abs/2010.11924 (2020)
2010 – 2019
- 2019
- [j1]Bryan M. Li
RL: Generic reinforcement learning codebase in TensorFlow. J. Open Source Softw. 4(42): 1524 (2019)
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