John Aslanides

According to our database, John Aslanides authored at least 17 papers between 2017 and 2022.

Collaborative distances:

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

2022

Fine-Tuning Language Models via Epistemic Neural Networks.

Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy, Nat McAleese, John Aslanides, Geoffrey Irving

CoRR, 2022

Improving alignment of dialogue agents via targeted human judgements.

Amelia Glaese, Nat McAleese, Maja Trebacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin J. Chadwick, Phoebe Thacker, Lucy Campbell-Gillingham, Jonathan Uesato, Po-Sen Huang, Ramona Comanescu, Fan Yang, Abigail See, Sumanth Dathathri, Rory Greig, Charlie Chen, Doug Fritz, Jaume Sanchez Elias, Richard Green, Sona Mokrá, Nicholas Fernando, Boxi Wu, Rachel Foley, Susannah Young, Iason Gabriel, William Isaac, John Mellor, Demis Hassabis, Koray Kavukcuoglu, Lisa Anne Hendricks, Geoffrey Irving

CoRR, 2022

Teaching language models to support answers with verified quotes.

Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, H. Francis Song, Martin J. Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, Nat McAleese

CoRR, 2022

Fine-tuning language models to find agreement among humans with diverse preferences.

Michiel A. Bakker, Martin J. Chadwick, Hannah Sheahan, Michael Henry Tessler, Lucy Campbell-Gillingham, Jan Balaguer, Nat McAleese, Amelia Glaese, John Aslanides, Matt M. Botvinick, Christopher Summerfield

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

Red Teaming Language Models with Language Models.

Ethan Perez, Saffron Huang, H. Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, Geoffrey Irving

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

2021

Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew J. Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving

CoRR, 2021

2020

Acme: A Research Framework for Distributed Reinforcement Learning.

Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal M. P. Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alexander Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Çaglar Gülçehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

CoRR, 2020

Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning.

Giambattista Parascandolo, Lars Buesing, Josh Merel, Leonard Hasenclever, John Aslanides, Jessica B. Hamrick, Nicolas Heess, Alexander Neitz, Theophane Weber

CoRR, 2020

Behaviour Suite for Reinforcement Learning.

Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt

Proceedings of the 8th International Conference on Learning Representations, 2020

A General Approach to Fairness with Optimal Transport.

Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

TF-Replicator: Distributed Machine Learning for Researchers.

Peter Buchlovsky, David Budden, Dominik Grewe, Chris Jones, John Aslanides, Frederic Besse, Andy Brock, Aidan Clark, Sergio Gomez Colmenarejo, Aedan Pope, Fabio Viola, Dan Belov

CoRR, 2019

When to use parametric models in reinforcement learning?

Hado van Hasselt, Matteo Hessel, John Aslanides

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Randomized Prior Functions for Deep Reinforcement Learning.

Ian Osband, John Aslanides, Albin Cassirer

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Generalised Discount Functions applied to a Monte-Carlo AImu Implementation.

Sean Lamont, John Aslanides, Jan Leike, Marcus Hutter

CoRR, 2017

AIXIjs: A Software Demo for General Reinforcement Learning.

John Aslanides

CoRR, 2017

Universal Reinforcement Learning Algorithms: Survey and Experiments.

John Aslanides, Jan Leike, Marcus Hutter

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Generalised Discount Functions applied to a Monte-Carlo AI u Implementation.

Sean Lamont, John Aslanides, Jan Leike, Marcus Hutter

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017