Edward Grefenstette

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 89

Top Venues

CoRR

ICLR

NeurIPS

ICLR (Poster)

Publications

Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu
Understanding the Effects of RLHF on LLM Generalisation and Diversity. ICLR (2024)
Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez
Debating with More Persuasive LLMs Leads to More Truthful Answers. CoRR (2024)
Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Scaling Opponent Shaping to High Dimensional Games. AAMAS (2024)
Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Tim Rocktäschel, Edward Grefenstette, David Scott Krueger
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. ICLR (2024)
Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian
H-GAP: Humanoid Control with a Generalist Planner. ICLR (2024)
Yicheng Luo, Jackie Kay, Edward Grefenstette, Marc Peter Deisenroth
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions. CoRR (2023)
Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth
Optimal Transport for Offline Imitation Learning. CoRR (2023)
Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth
Optimal Transport for Offline Imitation Learning. ICLR (2023)
Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs. NeurIPS (2023)
Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
Efficient Planning in a Compact Latent Action Space. ICLR (2023)
Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Scaling Opponent Shaping to High Dimensional Games. CoRR (2023)
Minqi Jiang, Michael Dennis, Edward Grefenstette, Tim Rocktäschel
minimax: Efficient Baselines for Autocurricula in JAX. CoRR (2023)
Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. J. Artif. Intell. Res. 76 (2023)
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu
Understanding the Effects of RLHF on LLM Generalisation and Diversity. CoRR (2023)
Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Edward Grefenstette, Tim Rocktäschel, David Scott Krueger
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. CoRR (2023)
Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian
H-GAP: Humanoid Control with a Generalist Planner. CoRR (2023)
Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk, Chris Lu, Edward Grefenstette, Tim Rocktäschel
Leading the Pack: N-player Opponent Shaping. CoRR (2023)
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette
Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS (2022)
Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel
Improving Policy Learning via Language Dynamics Distillation. CoRR (2022)
Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
Efficient Planning in a Compact Latent Action Space. CoRR (2022)
Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette
Large language models are not zero-shot communicators. CoRR (2022)
Minqi Jiang, Tim Rocktäschel, Edward Grefenstette
General Intelligence Requires Rethinking Exploration. CoRR (2022)
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Evolving Curricula with Regret-Based Environment Design. ICML (2022)
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoRR (2022)
Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel
Improving Policy Learning via Language Dynamics Distillation. NeurIPS (2022)
Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions. CoRR (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty for Unsupervised Environment Design. NeurIPS (2022)
Eric Hambro, Sharada P. Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski
Insights From the NeurIPS 2021 NetHack Challenge. CoRR (2022)
Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
Learning General World Models in a Handful of Reward-Free Deployments. CoRR (2022)
Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette
Improving Intrinsic Exploration with Language Abstractions. NeurIPS (2022)
Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
Evolving Curricula with Regret-Based Environment Design. CoRR (2022)
Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoLLAs (2022)
Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette
Improving Intrinsic Exploration with Language Abstractions. CoRR (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
Grounding Aleatoric Uncertainty in Unsupervised Environment Design. CoRR (2022)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel
Replay-Guided Adversarial Environment Design. CoRR (2021)
Eric Hambro, Sharada P. Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski
Insights From the NeurIPS 2021 NetHack Challenge. NeurIPS (Competition and Demos) (2021)
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. NeurIPS Datasets and Benchmarks (2021)
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette
Learning with AMIGo: Adversarially Motivated Intrinsic Goals. ICLR (2021)
Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Prioritized Level Replay. ICML (2021)
Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel
A Survey of Generalisation in Deep Reinforcement Learning. CoRR (2021)
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. CoRR (2021)
Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
Replay-Guided Adversarial Environment Design. NeurIPS (2021)
Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
Prioritized Level Replay. CoRR (2020)
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
Learning Reasoning Strategies in End-to-End Differentiable Proving. ICML (2020)
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel
The NetHack Learning Environment. CoRR (2020)
Victor Zhong, Tim Rocktäschel, Edward Grefenstette
RTFM: Generalising to New Environment Dynamics via Reading. ICLR (2020)
Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette
Differentiable Reasoning on Large Knowledge Bases and Natural Language. AAAI (2020)
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
Learning Reasoning Strategies in End-to-End Differentiable Proving. CoRR (2020)
Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette
Learning with AMIGo: Adversarially Motivated Intrinsic Goals. CoRR (2020)
Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel
The NetHack Learning Environment. NeurIPS (2020)
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav S. Sukhatme, Franziska Meier
Meta Learning via Learned Loss. ICPR (2020)
Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia
CompILE: Compositional Imitation Learning and Execution. ICML (2019)
Heinrich Küttler, Nantas Nardelli, Thibaut Lavril, Marco Selvatici, Viswanath Sivakumar, Tim Rocktäschel, Edward Grefenstette
TorchBeast: A PyTorch Platform for Distributed RL. CoRR (2019)
David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
Analysing Mathematical Reasoning Abilities of Neural Models. ICLR (Poster) (2019)
Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli
Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications. CVPR (2019)
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala
Generalized Inner Loop Meta-Learning. CoRR (2019)
Victor Zhong, Tim Rocktäschel, Edward Grefenstette
RTFM: Generalising to Novel Environment Dynamics via Reading. CoRR (2019)
Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette
Differentiable Reasoning on Large Knowledge Bases and Natural Language. CoRR (2019)
David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
Analysing Mathematical Reasoning Abilities of Neural Models. CoRR (2019)
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel
A Survey of Reinforcement Learning Informed by Natural Language. CoRR (2019)
Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications. CoRR (2019)
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Seyed Arian Hosseini, Pushmeet Kohli, Edward Grefenstette
Learning to Understand Goal Specifications by Modelling Reward. ICLR (Poster) (2019)
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel
A Survey of Reinforcement Learning Informed by Natural Language. IJCAI (2019)
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette
Learning to Follow Language Instructions with Adversarial Reward Induction. CoRR (2018)
Richard Evans, Edward Grefenstette
Learning Explanatory Rules from Noisy Data. J. Artif. Intell. Res. 61 (2018)
Richard Evans, Edward Grefenstette
Learning Explanatory Rules from Noisy Data (Extended Abstract). IJCAI (2018)
Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette
Jointly Learning "What" and "How" from Instructions and Goal-States. ICLR (Workshop) (2018)
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
Can Neural Networks Understand Logical Entailment? CoRR (2018)
Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. CoRR (2018)
Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia
Compositional Imitation Learning: Explaining and executing one task at a time. CoRR (2018)
Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
Can Neural Networks Understand Logical Entailment? ICLR (Poster) (2018)
Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
The NarrativeQA Reading Comprehension Challenge. Trans. Assoc. Comput. Linguistics 6 (2018)
Edward Grefenstette
Teaching Artificial Agents to Understand Language by Modelling Reward. CIKM (2018)
Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský
The Neural Noisy Channel. ICLR (Poster) (2017)
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
Learning to Compose Words into Sentences with Reinforcement Learning. ICLR (Poster) (2017)
Dzmitry Bahdanau, Tom Bosc, Stanislaw Jastrzebski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio
Learning to Compute Word Embeddings On the Fly. CoRR (2017)
Yishu Miao, Edward Grefenstette, Phil Blunsom
Discovering Discrete Latent Topics with Neural Variational Inference. CoRR (2017)
Yishu Miao, Edward Grefenstette, Phil Blunsom
Discovering Discrete Latent Topics with Neural Variational Inference. ICML (2017)
Xiaodan Zhu, Edward Grefenstette
Deep Learning for Semantic Composition. ACL (Tutorial Abstracts) (2017)
Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
The NarrativeQA Reading Comprehension Challenge. CoRR (2017)
Richard Evans, Edward Grefenstette
Learning Explanatory Rules from Noisy Data. CoRR (2017)
Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
Semantic Parsing with Semi-Supervised Sequential Autoencoders. CoRR (2016)
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio Gomez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John P. Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis
Hybrid computing using a neural network with dynamic external memory. Nat. 538 (7626) (2016)
Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
Learning to Compose Words into Sentences with Reinforcement Learning. CoRR (2016)
Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
Semantic Parsing with Semi-Supervised Sequential Autoencoders. EMNLP (2016)
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, Andrew W. Senior
Latent Predictor Networks for Code Generation. ACL (1) (2016)
Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský
The Neural Noisy Channel. CoRR (2016)
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Phil Blunsom
Reasoning about Entailment with Neural Attention. ICLR (Poster) (2016)
Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Andrew W. Senior, Fumin Wang, Phil Blunsom
Latent Predictor Networks for Code Generation. CoRR (2016)
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Teaching Machines to Read and Comprehend. NIPS (2015)
Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. CVSC (2015)
Edward Grefenstette, Mehrnoosh Sadrzadeh
Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning. Comput. Linguistics 41 (1) (2015)
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Learning to Transduce with Unbounded Memory. CoRR (2015)
Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
Teaching Machines to Read and Comprehend. CoRR (2015)
Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
Learning to Transduce with Unbounded Memory. NIPS (2015)
Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
A Convolutional Neural Network for Modelling Sentences. ACL (1) (2014)
Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
A Convolutional Neural Network for Modelling Sentences. CoRR (2014)
Edward Grefenstette, Karl Moritz Hermann, Georgiana Dinu, Phil Blunsom
New Directions in Vector Space Models of Meaning. ACL (Tutorial Abstracts) (2014)
Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann
A Deep Architecture for Semantic Parsing. CoRR (2014)
Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning. CoRR (2014)

Edward Grefenstette.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Edward Grefenstette