1 code implementation • 22 Feb 2022 • Nikolaus H. R. Howe, Simon Dufort-Labbé, Nitarshan Rajkumar, Pierre-Luc Bacon
We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments.
1 code implementation • NeurIPS 2021 • Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, Devon Hjelm, Philip Bachman, Aaron Courville
Data efficiency is a key challenge for deep reinforcement learning.
Ranked #3 on Atari Games 100k on Atari 100k (using extra training data)
1 code implementation • NeurIPS 2020 • Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
1 code implementation • 20 Sep 2022 • Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj, David Krueger, Sara Hooker
Modern machine learning research relies on relatively few carefully curated datasets.
no code implementations • ICLR Workshop SSL-RL 2021 • Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman, Aaron Courville
Data efficiency poses a major challenge for deep reinforcement learning.
no code implementations • 15 Mar 2022 • Nitarshan Rajkumar, Raymond Li, Dzmitry Bahdanau
We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model.
no code implementations • 23 Jan 2024 • Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung
Increased delegation of commercial, scientific, governmental, and personal activities to AI agents -- systems capable of pursuing complex goals with limited supervision -- may exacerbate existing societal risks and introduce new risks.