Browse by UCL people
Group by: Type | Date
Number of items: 65.
Article
Fan, A;
Urbanek, J;
Ringshia, P;
Dinan, E;
Qian, E;
Karamcheti, S;
Prabhumoye, S;
... Weston, J; + view all
(2020)
Generating Interactive Worlds with Text.
Proceedings of the AAAI Conference on Artificial Intelligence
, 34
(2)
pp. 1693-1700.
10.1609/aaai.v34i02.5532.
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Jiang, M;
Rocktäschel, T;
Grefenstette, E;
(2023)
General intelligence requires rethinking exploration.
Royal Society Open Science
, 10
(6)
, Article 230539. 10.1098/rsos.230539.
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Jiang, Z;
Minervini, P;
Jiang, M;
Rocktäschel, T;
(2021)
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.
arXiv
, Article arXiv:2102.04220.
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Kirk, Robert;
Zhang, Amy;
Grefenstette, Edward;
Rocktäschel, Tim;
(2023)
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning.
Journal of Artificial Intelligence Research
, 76
10.1613/jair.1.14174.
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Proceedings paper
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Ammanabrolu, P;
Urbanek, J;
Li, M;
Szlam, A;
Rocktäschel, T;
Weston, J;
(2021)
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds.
In: Toutanova, K and Rumshisky, A and Zettlemoyer, L and Hakkani-Tür, D and Beltagy, I and Bethard, S and Cotterell, R and Chakraborty, T and Zhou, Y, (eds.)
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
(pp. pp. 807-833).
Association for Computational Linguistics
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Augenstein, I;
Rocktäschel, T;
Vlachos, A;
Bontcheva, K;
(2016)
Stance Detection with Bidirectional Conditional Encoding.
In: Su, Jian and Duh, Kevin and Carreras, Xavier, (eds.)
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
(pp. pp. 876-885).
The Association for Computational Linguistics: Texas, USA.
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Bamford, C;
Jiang, M;
Samvelyan, M;
Rocktäschel, T;
(2022)
GriddlyJS: A Web IDE for Reinforcement Learning.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Bošnjak, M;
Rocktäschel, T;
Naradowsky, J;
Riedel, S;
(2017)
Programming with a differentiable forth interpreter.
In:
Proceedings of the 34th International Conference on Machine Learning.
(pp. pp. 547-556).
PMLR: Sydney, Australia.
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Bošnjak, M;
Rocktäschel, T;
Naradowsky, J;
Riedel, S;
(2017)
Programming with a differentiable forth interpreter.
In:
5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
International Conference on Representation Learning (ICRL): Toulon, France.
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Camburu, OM;
Rocktäschel, T;
Lukasiewicz, T;
Blunsom, P;
(2018)
E-SNLI: Natural language inference with natural language explanations.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.)
Advances in Neural Information Processing Systems 31.
(pp. pp. 9539-9549).
NIPS Proceedings: Montréal, Canada.
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Campero, A;
Raileanu, R;
Küttler, H;
Tenenbaum, JB;
Rocktäschel, T;
Grefenstette, E;
(2021)
Learning With AMIGo: Adversarially Motivated Intrinsic Goals.
In:
Proceedings of the 9th International Conference on Learning Representations (ICLR 2021).
International Conference on Learning Representations: Virtual.
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Campero, A;
Raileanu, R;
Küttler, H;
Tenenbaum, JB;
Rocktäschel, T;
Grefenstette, E;
(2021)
Learning with AMIGo: Adversarially Motivated Intrinsic Goals.
In:
(Proceedings) ICLR 2021: Ninth International Conference on Learning Representations.
OpenReview.net
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Cowen-Rivers, AI;
Minervini, P;
Rocktaschel, T;
Bosnjak, M;
Riedel, S;
Wang, J;
(2019)
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings.
In: Doran, Derek and Garcez, Artur d’Avila and Lecue, Freddy, (eds.)
Proceedings of the 2019 International Workshop on Neural- Symbolic Learning and Reasoning.
Annual workshop of the Neural-Symbolic Learning and Reasoning Association: Macao, China.
(In press).
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Daniluk, M;
Rocktäschel, T;
Welbl, J;
Riedel, S;
(2019)
Frustratingly short attention spans in neural language modeling.
In:
5th International Conference on Learning Representations (ICLR 2017) - Conference Track.
International Conference on Learning Representations (ICLR): Toulon, France.
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Demeester, T;
Rocktäschel, T;
Riedel, S;
(2016)
Lifted Rule Injection for Relation Embeddings.
In: Su, J and Duh, K and Carreras, X, (eds.)
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.
(pp. pp. 1389-1399).
Association for Computational Linguistics/Curran Associates
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Eisner, B;
Rocktäschel, T;
Augenstein, I;
Bošnjak, M;
Riedel, S;
(2016)
emoji2vec: Learning Emoji Representations from their Description.
In:
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media.
(pp. pp. 48-54).
Association for Computational Linguistics: Austin, TX, USA.
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Farquhar, G;
Rocktäschel, T;
Igl, M;
Whiteson, S;
(2018)
TreeqN and ATreEC: Differentiable tree-structured models for deep reinforcement learning.
In:
Proceedings of 6th International Conference on Learning Representations (ICLR 2018).
ICLR: Vancouver, BC, Canada.
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Foerster, J;
Farquhar, G;
Al-Shedivat, M;
Rocktäschel, T;
Xing, EP;
Whiteson, S;
(2018)
DiCE: The Infinitely Differentiable Monte-Carlo Estimator.
In: Dy, Jennifer and Krause, Andreas, (eds.)
Proceedings of the 35th International Conference on Machine Learning.
Proceedings of Machine Learning Research: Stockholm Sweden.
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Hambro, E;
Mohanty, S;
Babaev, D;
Byeon, M;
Chakraborty, D;
Grefenstette, E;
Jiang, M;
... Sypetkowski, M; + view all
(2022)
Insights from the NeurIPS 2021 NetHack Challenge.
In:
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track.
(pp. pp. 41-52).
Proceedings of Machine Learning Research (PMLR)
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Hambro, E;
Raileanu, R;
Rothermel, D;
Mella, V;
Rocktäschel, T;
Küttler, H;
Murray, N;
(2022)
Dungeons and Data: A Large-Scale NetHack Dataset.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Henaff, M;
Raileanu, R;
Jiang, M;
Rocktäschel, T;
(2022)
Exploration via Elliptical Episodic Bonuses.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Jain, S;
Kirk, R;
Lubana, ES;
Dick, RP;
Tanaka, H;
Grefenstette, E;
Rocktäschel, T;
(2024)
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks.
In:
12th International Conference on Learning Representations ICLR 2024.
ICLR
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Jiang, M;
Dennis, M;
Parker-Holder, J;
Foerster, J;
Grefenstette, E;
Rocktäschel, T;
(2021)
Replay-Guided Adversarial Environment Design.
In:
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
Neural Information Processing Systems: Sydney, Australia.
(In press).
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Jiang, M;
Dennis, M;
Parker-Holder, J;
Lupu, A;
Küttler, H;
Grefenstette, E;
Rocktäschel, T;
(2022)
Grounding Aleatoric Uncertainty for Unsupervised Environment Design.
In:
Advances in Neural Information Processing Systems.
NIPS
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Jiang, Z;
Xu, Y;
Wagener, N;
Luo, Y;
Janner, M;
Grefenstette, E;
Rocktäschel, T;
(2024)
H-GAP: Humanoid Control with a Generalist Planner.
In:
12th International Conference on Learning Representations ICLR 2024.
ICLR
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Jiang, Z;
Zhang, T;
Janner, M;
Li, Y;
Rocktäschel, T;
Grefenstette, E;
Tian, Y;
(2023)
Efficient Planning in a Compact Latent Action Space.
In:
11th International Conference on Learning Representations ICLR 2023.
ICLR
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Jiang, M;
Grefenstette, E;
Rocktäschel, T;
(2021)
Prioritized Level Replay.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 4940-4950).
PMLR: Proceedings of Machine Learning Research: Online Only.
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Jiang, Z;
Minervini, P;
Jiang, M;
Rocktäschel, T;
(2021)
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning.
In: Dignum, F and Lomuscio, A and Endriss, U and Nowé, A, (eds.)
AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems.
(pp. pp. 674-682).
ACM
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Kurin, V;
Igl, M;
Rocktäschel, T;
Boehmer, W;
Whiteson, S;
(2020)
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control.
In:
Proceedings of the 9th International Conference on Learning Representations (ICLR 2021).
International Conference on Learning Representations: Virtual conference.
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Küttler, H;
Nardelli, N;
Miller, AH;
Raileanu, R;
Selvatici, M;
Grefenstette, E;
Rocktäschel, T;
(2020)
The NetHack learning environment.
In:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
NeurIPS: Vancouver, Canada.
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Letcher, A;
Foerster, J;
Balduzzi, D;
Rocktäschel, T;
Whiteson, S;
(2019)
Stable Opponent Shaping in Differentiable Games.
In:
Proceedings of the 7th International Conference on Learning Representations:ICLR 2019.
ICLR: New Orleans, Louisiana,USA.
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Lewis, PSH;
Perez, E;
Piktus, A;
Petroni, F;
Karpukhin, V;
Goyal, N;
Küttler, H;
... Kiela, D; + view all
(2020)
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
In:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020.
NeurIPS
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Luketina, J;
Nardelli, N;
Farquhar, G;
Foerster, J;
Andreas, J;
Grefenstette, E;
Whiteson, S;
(2019)
A Survey of Reinforcement Learning Informed by Natural Language.
In: Eiter, Thomas and Kraus, Sarit, (eds.)
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19).
(pp. pp. 6309-6317).
AAAI Press (Association for the Advancement of Artificial Intelligence): Palo Alto, CA, USA.
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Mao, J;
Foerster, J;
Rocktäschel, T;
Al-Shedivat, M;
Farquhar, G;
Whiteson, S;
(2019)
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs.
In: Xing, Eric and Chaudhuri, Kamalika and Salakhutdinov, Rusian, (eds.)
Proceedings of the 36th International Conference on Machine Learning (ICML 2019).
Proceedings of Machine Learning Research (PMLR): Long Beach, CA, USA.
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Massarelli, L;
Petroni, F;
Piktus, A;
Ott, M;
Rocktäschel, T;
Plachouras, V;
Silvestri, F;
(2019)
How Decoding Strategies Affect the Verifiability of Generated Text.
In:
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020.
(pp. pp. 223-235).
Association for Computational Linguistics
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Matthews, M;
Samvelyan, M;
Parker-Holder, J;
Grefenstette, E;
Rocktäschel, T;
(2022)
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning.
In:
Proceedings of The 1st Conference on Lifelong Learning Agents.
(pp. pp. 856-874).
Proceedings of Machine Learning Research (PMLR)
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Mediratta, I;
Jiang, M;
Parker-Holder, J;
Dennis, M;
Vinitsky, E;
Rocktaschel, T;
(2023)
Stabilizing Unsupervised Environment Design with a Learned Adversary.
In:
Proceedings of The 2nd Conference on Lifelong Learning Agents, PMLR 232.
(pp. pp. 270-291).
PMLR: Proceedings of Machine Learning Research: McGill University, Montréal, Québec, Canada.
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Minervini, P;
Bosnjak, M;
Rocktäschel, T;
Riedel, S;
Towards Neural Theorem Proving at Scale.
In:
Proceedings of the 2nd Workshop on Neural Abstract Machines and Program Induction (NAMPI).
ICML workshop Neural Abstract Machines & Program Induction: Stockholm, Sweden.
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Minervini, P;
Bosnjak, M;
Rocktäschel, T;
Riedel, S;
Grefenstette, E;
(2020)
Differentiable Reasoning on Large Knowledge Bases and Natural Language.
In:
Proceedings of The Thirty-fourth AAAI Conference on Artificial Intelligence The Thirty-second Innovative Applications Of Artificial Intelligence Conference, The Tenth AAAI Symposium on Educational Advances In Artificial Intelligence.
(pp. pp. 5182-5190).
AAAI Press: New York, NY, USA.
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Minervini, P;
Demeester, T;
Rocktäschel, T;
Riedel, S;
(2017)
Adversarial Sets for Regularising Neural Link Predictors.
In: Elidan, Gal and Kersting, Kristian and Ihler, Alexander, (eds.)
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI 2017).
Curran Associates Inc: Red Hook, NY, USA.
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Minervini, P;
Riedel, S;
Stenetorp, P;
Grefenstette, E;
Rocktäschel, T;
(2020)
Learning Reasoning Strategies in End-to-End Differentiable Proving.
In:
Proceedings of the 37th International Conference on Machine Learning, PMLR 119.
(pp. pp. 6938-6949).
PMLR
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Mu, J;
Zhong, V;
Raileanu, R;
Jiang, M;
Goodman, N;
Rocktäschel, T;
Grefenstette, E;
(2022)
Improving Intrinsic Exploration with Language Abstractions.
In:
Advances in Neural Information Processing Systems.
NIPS
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Paglieri, D;
Cupiał, B;
Coward, S;
Piterbarg, U;
Wolczyk, M;
Khan, A;
Pignatelli, E;
... Rocktäschel, T; + view all
(2025)
BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games.
In:
13th International Conference on Learning Representations ICLR 2025.
(pp. pp. 36061-36097).
ICLR
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Parker-Holder, J;
Jiang, M;
Dennis, M;
Samvelyan, M;
Foerster, J;
Grefenstette, E;
Rocktäschel, T;
(2022)
Evolving Curricula with Regret-Based Environment Design.
In:
Proceedings of Machine Learning Research.
(pp. pp. 17473-17498).
Proceedings of Machine Learning Research (PMLR)
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Petroni, F;
Lewis, PSH;
Piktus, A;
Rocktäschel, T;
Wu, Y;
Miller, AH;
Riedel, S;
(2020)
How Context Affects Language Models' Factual Predictions.
In: McCallum, Andrew and Singh, Sameer and Halevy, Alon, (eds.)
Proceedings of the Automated Knowledge Base Construction (AKBC) 2020.
AKBC
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Petroni, F;
Piktus, A;
Fan, A;
Lewis, PSH;
Yazdani, M;
Cao, ND;
Thorne, J;
... Riedel, S; + view all
(2021)
KILT: a Benchmark for Knowledge Intensive Language Tasks.
In: Toutanova, K and Rumshisky, A and Zettlemoyer, L and Hakkani-Tür, D and Beltagy, I and Bethard, S and Cotterell, R and Chakraborty, T and Zhou, Y, (eds.)
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
(pp. pp. 2523-2544).
Association for Computational Linguistics
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Petroni, F;
Rocktäschel, T;
Lewis, P;
Bakhtin, A;
Wu, Y;
Miller, AH;
Riedel, S;
(2019)
Language Models as Knowledge Bases?
In:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.
(pp. pp. 2463-2473).
Association for Computational Linguistics: Hong Kong, China.
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Raileanu, R;
Rocktäschel, T;
(2020)
RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments.
In:
Proceedings of the 08th International Conference on Learning Representations 2020.
ICLR
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Rocktäschel, T;
Riedel, S;
(2017)
End-to-end differentiable proving.
In: Guyon, Isabelle and von Luxburg, Ulrike, (eds.)
NIPS'17 Proceedings of the 31st International Conference on Neural Information Processing Systems.
(pp. pp. 3791-3803).
ACM (Association for Computing Machinery): New York, USA.
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Ruis, L;
Mozes, M;
Bae, J;
Kamalakara, SR;
Talupuru, D;
Locatelli, A;
Kirk, R;
... Bartolo, M; + view all
(2025)
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models.
In:
13th International Conference on Learning Representations ICLR 2025.
(pp. pp. 1932-1994).
ICLR
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Ruis, Laura;
Khan, Akbir;
Biderman, Stella;
Hooker, Sara;
Rocktaschel, Tim;
Grefenstette, Edward;
(2023)
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs.
In: Oh, A and Neumann, T and Globerson, A and Saenko, K and Hardt, M and Levine, S, (eds.)
Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS).
Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans, LA, USA.
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Rutherford, A;
Ellis, B;
Gallici, M;
Cook, J;
Lupu, A;
Ingvarsson, G;
Willi, T;
... Foerster, J; + view all
(2024)
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Advances in Neural Information Processing Systems.
NeurIPS
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Saeidi, M;
Bartolo, M;
Lewis, P;
Singh, S;
Rocktäschel, T;
Sheldon, M;
Bouchard, G;
(2018)
Interpretation of Natural Language Rules in Conversational Machine Reading.
In:
Proceedings of 2018 Conference on Empirical Methods in Na Language Processing, EMNLP 2018.
(pp. pp. 2087-2097).
ACL: Brussels, Belgium.
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Samvelyan, M;
Khan, A;
Dennis, M;
Jiang, M;
Parker-Holder, J;
Foerster, J;
Raileanu, R;
(2023)
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning.
In:
Proceedings of the Eleventh International Conference on Learning Representations.
: Kigali, Rwanda.
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Samvelyan, M;
Kirk, R;
Kurin, V;
Parker-Holder, J;
Jiang, M;
Hambro, E;
Petroni, F;
... Rocktäschel, T; + view all
(2021)
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research.
In:
Advances in Neural Information Processing Systems 34 pre-proceedings.
(In press).
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Samvelyan, M;
Raparthy, SC;
Lupu, A;
Hambro, E;
Markosyan, AH;
Bhatt, M;
Mao, Y;
... Raileanu, R; + view all
(2024)
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts.
In: Globersons, Amir and Mackey, Lester and Belgrave, Danielle and Fan, Angela and Paquet, Ulrich and Tomczak, Jakub M and Zhang, Cheng, (eds.)
Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
NeurIPS (Neural Information Processing Systems)
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Sivakumar, V;
Rocktäschel, T;
Miller, AH;
Küttler, H;
Nardelli, N;
Rabbat, M;
Pineau, J;
MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
ML for Systems: Vancouver, Canada.
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Stacey, J;
Minervini, P;
Dubossarsky, H;
Riedel, S;
Rocktäschel, T;
(2020)
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training.
In: Webber, B and Cohn, T and He, Y and Liu, Y, (eds.)
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
(pp. pp. 8281-8291).
Association for Computational Linguistics: Stroudsburg, PA, USA.
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Weber, L;
Minervini, P;
Münchmeyer, J;
Leser, U;
Rocktäschel, T;
(2019)
NLprolog: Reasoning with Weak Unification for Question Answering in Natural Language.
In: Marquez, Lluis and Korhonen, Anna and Traum, David, (eds.)
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019).
(pp. pp. 6151-6161).
ACL (Association for Computational Linguistics): Florence, Italy.
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Weissenborn, D;
Minervini, P;
Augenstein, I;
Welbl, J;
Rocktaschel, T;
Bosnjak, M;
Mitchell, J;
... Riedel, S; + view all
(2018)
Jack the Reader - A Machine Reading Framework.
In:
(Proceedings) Proceedings of ACL 2018, System Demonstrations.
Association for Computational Linguistics (ACL)
(In press).
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Xu, Y;
Rybkin, O;
Parker-Holder, J;
Roberts, SJ;
Pacchiano, A;
Rocktäschel, T;
Ball, PJ;
(2022)
Learning General World Models in a Handful of Reward-Free Deployments.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Zhong, V;
Mu, J;
Zettlemoyer, L;
Grefenstette, E;
Rocktäschel, T;
(2022)
Improving Policy Learning via Language Dynamics Distillation.
In:
Advances in Neural Information Processing Systems.
NeurIPS
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Zhong, V;
Rocktäschel, T;
Grefenstette, E;
(2020)
RTFM: Generalising to New Environment Dynamics via Reading.
In:
Proceedings of the International Conference on Learning Representations (ICLR 2020).
(pp. pp. 1-17).
ICLR
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Working / discussion paper
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Urbanek, J;
Fan, A;
Karamcheti, S;
Jain, S;
Humeau, S;
Dinan, E;
Rocktäschel, T;
... Weston, J; + view all
(2019)
Learning to speak and act in a fantasy text adventure game.
ArXiv
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Thesis
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Rocktaschel, Tim;
(2018)
Combining Representation Learning with Logic for Language Processing.
Doctoral thesis (Ph.D), UCL (University College London).
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