Edward Grefenstette

139 papers A* 28A 2B 1Journal 77Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Subgoal-driven Framework for Improving Long-Horizon LLM Agents. CoRR Taiyi Wang, Sian Gooding, Florian Hartmann, Oriana Riva, Edward Grefenstette
2026	J	`jnl`	Improving Interactive In-Context Learning from Natural Language Feedback. CoRR Martin Klissarov, Jonathan Cook, Diego Antognini, Hao Sun, Jingling Li, Natasha Jaques, Claudiu Musat, Edward Grefenstette
2026	J	`jnl`	Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions. CoRR J. Rosser, Robert Kirk, Edward Grefenstette, Jakob N. Foerster, Laura Ruis
2026	J	`jnl`	Learning to Learn from Language Feedback with Social Meta-Learning. CoRR Jonathan Cook, Diego Antognini, Martin Klissarov, Claudiu Musat, Edward Grefenstette
2025	J	`jnl`	Generative Data Refinement: Just Ask for Better Data. CoRR Minqi Jiang, João G. M. Araújo, Will Ellsworth, Sian Gooding, Edward Grefenstette
2025	J	`jnl`	Interaction Dynamics as a Reward Signal for LLMs. CoRR Sian Gooding, Edward Grefenstette
2025	J	`jnl`	Learning When to Plan: Efficiently Allocating Test-Time Compute for LLM Agents. CoRR Davide Paglieri, Bartlomiej Cupial, Jonathan Cook, Ulyana Piterbarg, Jens Tuyls, Edward Grefenstette, Jakob Nicolaus Foerster, Jack Parker-Holder, Tim Rocktäschel
2025	A*	`conf`	Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. ICLR Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara, Dwaraknath Gnaneshwar, Acyr Locatelli, Robert Kirk, Tim Rocktäschel, Edward Grefenstette, Max Bartolo
2025	J	`jnl`	Writing as a testbed for open ended agents. CoRR Sian Gooding, Lucia Lopez-Rivilla, Edward Grefenstette
2024	J	`jnl`	Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL. CoRR Eduardo Pignatelli, Johan Ferret, Tim Rocktäschel, Edward Grefenstette, Davide Paglieri, Samuel Coward, Laura Toni
2024	A*	`conf`	Debating with More Persuasive LLMs Leads to More Truthful Answers. ICML Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez
2024	J	`jnl`	Debating with More Persuasive LLMs Leads to More Truthful Answers. CoRR Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez
2024	A*	`conf`	H-GAP: Humanoid Control with a Generalist Planner. ICLR Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian
2024	A*	`conf`	Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. ICLR Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Tim Rocktäschel, Edward Grefenstette, David Scott Krueger
2024	J	`jnl`	Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models. CoRR Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara, Dwarak Talupuru, Acyr Locatelli, Robert Kirk, Tim Rocktäschel, Edward Grefenstette, Max Bartolo
2024	A	`conf`	Scaling Opponent Shaping to High Dimensional Games. AAMAS Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
2024	A*	`conf`	Understanding the Effects of RLHF on LLM Generalisation and Diversity. ICLR Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu
2023	J	`jnl`	A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. J. Artif. Intell. Res. Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel
2023	A*	`conf`	Efficient Planning in a Compact Latent Action Space. ICLR Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
2023	J	`jnl`	Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions. CoRR Yicheng Luo, Jackie Kay, Edward Grefenstette, Marc Peter Deisenroth
2023	J	`jnl`	H-GAP: Humanoid Control with a Generalist Planner. CoRR Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian
2023	J	`jnl`	Leading the Pack: N-player Opponent Shaping. CoRR Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk, Chris Lu, Edward Grefenstette, Tim Rocktäschel
2023	J	`jnl`	Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. CoRR Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Edward Grefenstette, Tim Rocktäschel, David Scott Krueger
2023	A*	`conf`	Optimal Transport for Offline Imitation Learning. ICLR Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth
2023	J	`jnl`	Optimal Transport for Offline Imitation Learning. CoRR Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth
2023	—	`ed.`	Proceedings of the 8th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2023, Toronto, Canada, July 13, 2023. RepL4NLP@ACL Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita
2023	J	`jnl`	Scaling Opponent Shaping to High Dimensional Games. CoRR Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
2023	A*	`conf`	The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs. NeurIPS Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette
2023	J	`jnl`	Understanding the Effects of RLHF on LLM Generalisation and Diversity. CoRR Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu
2023	J	`jnl`	minimax: Efficient Baselines for Autocurricula in JAX. CoRR Minqi Jiang, Michael Dennis, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	Efficient Planning in a Compact Latent Action Space. CoRR Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian
2022	A*	`conf`	Evolving Curricula with Regret-Based Environment Design. ICML Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	Evolving Curricula with Regret-Based Environment Design. CoRR Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	General Intelligence Requires Rethinking Exploration. CoRR Minqi Jiang, Tim Rocktäschel, Edward Grefenstette
2022	J	`jnl`	Graph Backup: Data Efficient Backup Exploiting Markovian Transitions. CoRR Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette
2022	A*	`conf`	Grounding Aleatoric Uncertainty for Unsupervised Environment Design. NeurIPS Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
2022	J	`jnl`	Grounding Aleatoric Uncertainty in Unsupervised Environment Design. CoRR Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster
2022	—	`conf`	Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoLLAs Michael T. Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoRR Michael T. Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel
2022	A*	`conf`	Improving Intrinsic Exploration with Language Abstractions. NeurIPS Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette
2022	J	`jnl`	Improving Intrinsic Exploration with Language Abstractions. CoRR Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette
2022	A*	`conf`	Improving Policy Learning via Language Dynamics Distillation. NeurIPS Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	Improving Policy Learning via Language Dynamics Distillation. CoRR Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel
2022	J	`jnl`	Insights From the NeurIPS 2021 NetHack Challenge. CoRR 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
2022	J	`jnl`	Large language models are not zero-shot communicators. CoRR Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette
2022	A*	`conf`	Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette
2022	J	`jnl`	Learning General World Models in a Handful of Reward-Free Deployments. CoRR Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette
2022	—	`ed.`	Proceedings of the 7th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2022, Dublin, Ireland, May 26, 2022 RepL4NLP@ACL Spandana Gella, He He, Bodhisattwa Prasad Majumder, Burcu Can, Eleonora Giunchiglia, Samuel Cahyawijaya, Sewon Min, Maximilian Mozes, Xiang Lorraine Li, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Laura Rimell, Chris Dyer
2021	J	`jnl`	A Survey of Generalisation in Deep Reinforcement Learning. CoRR Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel
2021	—	`conf`	Insights From the NeurIPS 2021 NetHack Challenge. NeurIPS (Competition and Demos) 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
2021	—	`ch.`	Learning Reasoning Strategies in End-to-End Differentiable Proving. Neuro-Symbolic Artificial Intelligence Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
2021	A*	`conf`	Learning with AMIGo: Adversarially Motivated Intrinsic Goals. ICLR Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette
2021	—	`conf`	MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. NeurIPS Datasets and Benchmarks Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
2021	J	`jnl`	MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. CoRR Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel
2021	A*	`conf`	Prioritized Level Replay. ICML Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
2021	A*	`conf`	Replay-Guided Adversarial Environment Design. NeurIPS Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
2021	J	`jnl`	Replay-Guided Adversarial Environment Design. CoRR Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel
2020	A*	`conf`	Differentiable Reasoning on Large Knowledge Bases and Natural Language. AAAI Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette
2020	—	`ch.`	Differentiable Reasoning on Large Knowledge Bases and Natural Language. Knowledge Graphs for eXplainable Artificial Intelligence Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette
2020	A*	`conf`	Learning Reasoning Strategies in End-to-End Differentiable Proving. ICML Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
2020	J	`jnl`	Learning Reasoning Strategies in End-to-End Differentiable Proving. CoRR Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel
2020	J	`jnl`	Learning with AMIGo: Adversarially Motivated Intrinsic Goals. CoRR Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette
2020	B	`conf`	Meta Learning via Learned Loss. ICPR Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav S. Sukhatme, Franziska Meier
2020	J	`jnl`	Prioritized Level Replay. CoRR Minqi Jiang, Edward Grefenstette, Tim Rocktäschel
2020	A*	`conf`	RTFM: Generalising to New Environment Dynamics via Reading. ICLR Victor Zhong, Tim Rocktäschel, Edward Grefenstette
2020	A*	`conf`	The NetHack Learning Environment. NeurIPS Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel
2020	J	`jnl`	The NetHack Learning Environment. CoRR Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel
2019	A*	`conf`	A Survey of Reinforcement Learning Informed by Natural Language. IJCAI Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel
2019	J	`jnl`	A Survey of Reinforcement Learning Informed by Natural Language. CoRR Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel
2019	—	`conf`	Analysing Mathematical Reasoning Abilities of Neural Models. ICLR (Poster) David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
2019	J	`jnl`	Analysing Mathematical Reasoning Abilities of Neural Models. CoRR David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli
2019	A*	`conf`	CompILE: Compositional Imitation Learning and Execution. ICML Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia
2019	J	`jnl`	Differentiable Reasoning on Large Knowledge Bases and Natural Language. CoRR Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette
2019	J	`jnl`	Generalized Inner Loop Meta-Learning. CoRR Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala
2019	A*	`conf`	Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications. CVPR Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli
2019	J	`jnl`	Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications. CoRR Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli
2019	—	`conf`	Learning to Understand Goal Specifications by Modelling Reward. ICLR (Poster) Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Seyed Arian Hosseini, Pushmeet Kohli, Edward Grefenstette
2019	J	`jnl`	RTFM: Generalising to Novel Environment Dynamics via Reading. CoRR Victor Zhong, Tim Rocktäschel, Edward Grefenstette
2019	J	`jnl`	TorchBeast: A PyTorch Platform for Distributed RL. CoRR Heinrich Küttler, Nantas Nardelli, Thibaut Lavril, Marco Selvatici, Viswanath Sivakumar, Tim Rocktäschel, Edward Grefenstette
2018	—	`conf`	Can Neural Networks Understand Logical Entailment? ICLR (Poster) Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
2018	J	`jnl`	Can Neural Networks Understand Logical Entailment? CoRR Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
2018	J	`jnl`	Compositional Imitation Learning: Explaining and executing one task at a time. CoRR Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia
2018	—	`conf`	Jointly Learning "What" and "How" from Instructions and Goal-States. ICLR (Workshop) Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette
2018	A*	`conf`	Learning Explanatory Rules from Noisy Data (Extended Abstract). IJCAI Richard Evans, Edward Grefenstette
2018	J	`jnl`	Learning Explanatory Rules from Noisy Data. J. Artif. Intell. Res. Richard Evans, Edward Grefenstette
2018	J	`jnl`	Learning to Follow Language Instructions with Adversarial Reward Induction. CoRR Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette
2018	J	`jnl`	Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. CoRR Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli
2018	A	`conf`	Teaching Artificial Agents to Understand Language by Modelling Reward. CIKM Edward Grefenstette
2018	J	`jnl`	The NarrativeQA Reading Comprehension Challenge. Trans. Assoc. Comput. Linguistics Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
2017	—	`conf`	Deep Learning for Semantic Composition. ACL (Tutorial Abstracts) Xiaodan Zhu, Edward Grefenstette
2017	A*	`conf`	Discovering Discrete Latent Topics with Neural Variational Inference. ICML Yishu Miao, Edward Grefenstette, Phil Blunsom
2017	J	`jnl`	Discovering Discrete Latent Topics with Neural Variational Inference. CoRR Yishu Miao, Edward Grefenstette, Phil Blunsom
2017	J	`jnl`	Learning Explanatory Rules from Noisy Data. CoRR Richard Evans, Edward Grefenstette
2017	—	`conf`	Learning to Compose Words into Sentences with Reinforcement Learning. ICLR (Poster) Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
2017	J	`jnl`	Learning to Compute Word Embeddings On the Fly. CoRR Dzmitry Bahdanau, Tom Bosc, Stanislaw Jastrzebski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio
2017	—	`ed.`	Proceedings of the 2nd Workshop on Representation Learning for NLP, Rep4NLP@ACL 2017, Vancouver, Canada, August 3, 2017 Rep4NLP@ACL Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay B. Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih
2017	J	`jnl`	The NarrativeQA Reading Comprehension Challenge. CoRR Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette
2017	—	`conf`	The Neural Noisy Channel. ICLR (Poster) Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský
2016	J	`jnl`	Hybrid computing using a neural network with dynamic external memory. Nat. 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
2016	—	`conf`	Latent Predictor Networks for Code Generation. ACL (1) Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, Andrew W. Senior
2016	J	`jnl`	Latent Predictor Networks for Code Generation. CoRR Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Andrew W. Senior, Fumin Wang, Phil Blunsom
2016	J	`jnl`	Learning to Compose Words into Sentences with Reinforcement Learning. CoRR Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
2016	—	`ed.`	Proceedings of the 1st Workshop on Representation Learning for NLP, Rep4NLP@ACL 2016, Berlin, Germany, August 11, 2016 Rep4NLP@ACL Phil Blunsom, Kyunghyun Cho, Shay B. Cohen, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Wen-tau Yih
2016	A*	`conf`	Reasoning about Entailment with Neural Attention. ICLR (Poster) Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Phil Blunsom
2016	A*	`conf`	Semantic Parsing with Semi-Supervised Sequential Autoencoders. EMNLP Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
2016	J	`jnl`	Semantic Parsing with Semi-Supervised Sequential Autoencoders. CoRR Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann
2016	J	`jnl`	The Neural Noisy Channel. CoRR Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský
2015	J	`jnl`	Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning. Comput. Linguistics Edward Grefenstette, Mehrnoosh Sadrzadeh
2015	—	`conf`	Learning to Transduce with Unbounded Memory. NIPS Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
2015	J	`jnl`	Learning to Transduce with Unbounded Memory. CoRR Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom
2015	—	`ed.`	Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, July 26-31, 2015 CVSC Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih
2015	—	`conf`	Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. CVSC Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih
2015	—	`conf`	Teaching Machines to Read and Comprehend. NIPS Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
2015	J	`jnl`	Teaching Machines to Read and Comprehend. CoRR Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom
2014	—	`conf`	A Convolutional Neural Network for Modelling Sentences. ACL (1) Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
2014	J	`jnl`	A Convolutional Neural Network for Modelling Sentences. CoRR Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom
2014	J	`jnl`	A Deep Architecture for Semantic Parsing. CoRR Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann
2014	J	`jnl`	Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning. CoRR Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette
2014	—	`conf`	New Directions in Vector Space Models of Meaning. ACL (Tutorial Abstracts) Edward Grefenstette, Karl Moritz Hermann, Georgiana Dinu, Phil Blunsom
2014	—	`ed.`	Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality, CVSC@EACL 2014, Gothenburg, Sweden, April 26-30, 2014 CVSC@EACL Alexandre Allauzen, Raffaella Bernardi, Edward Grefenstette, Hugo Larochelle, Christopher D. Manning, Scott Wen-tau Yih
2013	—	`conf`	"Not not bad" is not "bad": A distributional account of negation. CVSM@ACL Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom
2013	J	`jnl`	"Not not bad" is not "bad": A distributional account of negation. CoRR Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom
2013	J	`jnl`	A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure CoRR Stephen Clark, Bob Coecke, Edward Grefenstette, Stephen Pulman, Mehrnoosh Sadrzadeh
2013	J	`jnl`	Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics. CoRR Edward Grefenstette
2013	J	`jnl`	Lambek vs. Lambek: Functorial Vector Space Semantics and String Diagrams for Lambek Calculus CoRR Bob Coecke, Edward Grefenstette, Mehrnoosh Sadrzadeh
2013	J	`jnl`	Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus. Ann. Pure Appl. Log. Bob Coecke, Edward Grefenstette, Mehrnoosh Sadrzadeh
2013	J	`jnl`	Multi-Step Regression Learning for Compositional Distributional Semantics CoRR Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang, Mehrnoosh Sadrzadeh, Marco Baroni
2013	—	`conf`	Multi-Step Regression Learning for Compositional Distributional Semantics. IWCS Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang, Mehrnoosh Sadrzadeh, Marco Baroni
2013	—	`book`	Quantum Physics and Linguistics - A Compositional, Diagrammatic Discourse. Quantum Physics and Linguistics Chris Heunen, Mehrnoosh Sadrzadeh, Edward Grefenstette
2013	J	`jnl`	Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors CoRR Edward Grefenstette
2013	—	`conf`	Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors. *SEM@NAACL-HLT Edward Grefenstette
2011	—	`conf`	A Compositional Distributional Semantics, Two Concrete Constructions, and Some Experimental Evaluations. QI Mehrnoosh Sadrzadeh, Edward Grefenstette
2011	J	`jnl`	A Compositional Distributional Semantics, Two Concrete Constructions, and some Experimental Evaluations CoRR Mehrnoosh Sadrzadeh, Edward Grefenstette
2011	J	`jnl`	Concrete Sentence Spaces for Compositional Distributional Models of Meaning CoRR Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke, Stephen Pulman
2011	—	`conf`	Concrete Sentence Spaces for Compositional Distributional Models of Meaning. IWCS Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke, Stephen Pulman
2011	J	`jnl`	Experimental Support for a Categorical Compositional Distributional Model of Meaning CoRR Edward Grefenstette, Mehrnoosh Sadrzadeh
2011	A*	`conf`	Experimental Support for a Categorical Compositional Distributional Model of Meaning. EMNLP Edward Grefenstette, Mehrnoosh Sadrzadeh
2011	J	`jnl`	Experimenting with Transitive Verbs in a DisCoCat CoRR Edward Grefenstette, Mehrnoosh Sadrzadeh
2011	—	`conf`	Experimenting with transitive verbs in a DisCoCat. GEMS Edward Grefenstette, Mehrnoosh Sadrzadeh