generated by bibbase.org
  2024 (13)
Scaling Opponent Shaping to High Dimensional Games. Khan, A.; Willi, T.; Kwan, N.; Tachetti, A.; Lu, C.; Grefenstette, E.; Rocktäschel, T.; and Foerster, J. In AAMAS 2024 (𝗢𝗿𝗮𝗹), 2024.
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Promptbreeder: Self-Referential Self-Improvement via Prompt Evolution. Fernando, C.; Banarse, D.; Michalewski, H.; Osindero, S.; and Rocktäschel, T. In ICML 2024, 2024.
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Multi-Agent Diagnostics for Robustness via Illuminated Diversity. Samvelyan, M.; Paglieri, D.; Jiang, M.; Parker-Holder, J.; and Rocktäschel, T. In AAMAS 2024 (𝗢𝗿𝗮𝗹), 2024.
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JaxMARL: Multi-Agent RL Environments and Algorithms in JAX. Rutherford, A.; Ellis, B.; Gallici, M.; Cook, J.; Lupu, A.; Ingvarsson, G.; Willi, T.; Khan, A.; Schroeder de Witt, C.; Souly, A.; and others In AAMAS 2024, 2024.
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H-GAP: Humanoid Control with a Generalist Planner. Jiang, Z.; Xu, Y.; Wagener, N.; Luo, Y.; Janner, M.; Grefenstette, E.; Rocktäschel, T.; and Tian, Y. In ICLR 2024 (𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁), 2024.
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Mechanistically Analyzing the Effects of Fine-tuning on Procedurally Defined Tasks. Jain, S.; Kirk, R.; Lubana, E. S.; Dick, R. P; Tanaka, H.; Rocktäschel, T.; Grefenstette, E.; and Krueger, D. S. In ICLR 2024, 2024.
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Debating with More Persuasive LLMs Leads to More Truthful Answers. Khan, A.; Hughes, J.; Valentine, D.; Ruis, L.; Sachan, K.; Radhakrishnan, A.; Grefenstette, E.; Bowman, S. R; Rocktäschel, T.; and Perez, E. In ICML 2024 (𝗢𝗿𝗮𝗹), 2024.
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Genie: Generative Interactive Environments. Bruce, J.; Dennis, M.; Edwards, A.; Parker-Holder, J.; Shi, Y.; Hughes, E.; Lai, M.; Mavalankar, A.; Steigerwald, R.; Apps, C.; and others In ICML 2024 (𝗢𝗿𝗮𝗹), 2024.
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Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts. Samvelyan, M.; Raparthy, S. C.; Lupu, A.; Hambro, E.; Markosyan, A. H; Bhatt, M.; Mao, Y.; Jiang, M.; Parker-Holder, J.; Foerster, J.; and others In SeT LLM, 2024.
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Outliers and Calibration Sets have Diminishing Effect on Quantization of Modern LLMs. Paglieri, D.; Dash, S.; Rocktäschel, T.; and Parker-Holder, J. In ES-FoMo-II 2024, 2024.
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Open-Endedness is Essential for Artificial Superhuman Intelligence. Hughes, E.; Dennis, M.; Parker-Holder, J.; Behbahani, F.; Mavalankar, A.; Shi, Y.; Schaul, T.; and Rocktäschel, T. In ICML 2024 (𝗢𝗿𝗮𝗹), 2024.
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Artificial Intelligence: 10 Things You Should Know. Rocktäschel, T. 2024.
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Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL. Pignatelli, E.; Ferret, J.; Paglieri, D.; Coward, S.; Rocktäschel, T.; Grefenstette, E.; and Toni, L. In AutoRL 2024, 2024.
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  2023 (13)
A Survey of Zero-Shot Generalisation in Deep Reinforcement Learning. Kirk, R.; Zhang, A.; Grefenstette, E.; and Rocktäschel, T. JAIR. 2023.
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Efficient Planning in a Compact Latent Action Space. Jiang, Z.; Zhang, T.; Janner, M.; Li, Y.; Rocktäschel, T.; Grefenstette, E.; and Tian, Y. In ICLR 2023, 2023.
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The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs. Ruis, L.; Khan, A.; Biderman, S.; Hooker, S.; Rocktäschel, T.; and Grefenstette, E. In NeurIPS 2023 (𝗦𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁), 2023.
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General Intelligence Requires Rethinking Exploration. Jiang, M.; Rocktäschel, T.; and Grefenstette, E. Royal Society Open Science. 2023.
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MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. Samvelyan, M.; Khan, A.; Dennis, M. D; Jiang, M.; Parker-Holder, J.; Foerster, J. N.; Raileanu, R.; and Rocktäschel, T. In ICLR 2023, 2023.
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Human-Timescale Adaptation in an Open-Ended Task Space. Team, A. A.; Bauer, J.; Baumli, K.; Baveja, S.; Behbahani, F.; Bhoopchand, A.; Bradley-Schmieg, N.; Chang, M.; Clay, N.; Collister, A.; and others In ICML 2023 (𝗢𝗿𝗮𝗹), 2023.
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Stabilizing Unsupervised Environment Design with a Learned Adversary. Mediratta, I.; Jiang, M.; Parker-Holder, J.; D Dennis, M.; Vinitsky, E.; and Rocktäschel, T. In CoLLAs 2023, 2023.
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Do LLMs Selectively Encode the Goal of an Agent's Reach?. Ruis, L.; Findeis, A.; Bradley, H.; Rahmani, H. A.; Choe, K. W.; Grefenstette, E.; and Rocktäschel, T. In ToM 2023, 2023.
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minimax: Efficient Baselines for Autocurricula in JAX. Jiang, M.; Dennis, M. D; Grefenstette, E.; and Rocktäschel, T. In ALOE 2023, 2023.
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Mix-ME: Quality-Diversity for Multi-Agent Learning. Ingvarsson, G.; Samvelyan, M.; Flageat, M.; Lim, B.; Cully, A.; and Rocktäschel, T. In ALOE 2023, 2023.
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Leading the Pack: N-player Opponent Shaping. Souly, A.; Willi, T.; Khan, A.; Kirk, R.; Lu, C.; Grefenstette, E.; and Rocktäschel, T. In MASEC 2023, 2023.
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Vision-Language Models as a Source of Rewards. Baumli, K.; Baveja, S.; Behbahani, F.; Chan, H.; Comanici, G.; Flennerhag, S.; Gazeau, M.; Holsheimer, K.; Horgan, D.; Laskin, M.; and others In ALOE 2023, 2023.
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ChatArena: Multi-agent Language Game Environments for Large Language Models. Wu, Y.; Jiang, Z.; Khan, A.; Fu, Y.; Ruis, L.; Grefenstette, E.; and Rocktäschel, T. 2023.
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  2022 (13)
Grounding Aleatoric Uncertainty in Unsupervised Environment Design. Jiang, M.; Dennis, M. D; Parker-Holder, J.; Lupu, A.; Küttler, H.; Grefenstette, E.; Rocktäschel, T.; and Foerster, J. N. In NeurIPS 2022, 2022.
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Generalization in Cooperative Multi-agent Systems. Mahajan, A.; Samvelyan, M.; Gupta, T.; Ellis, B.; Sun, M.; Rocktäschel, T.; and Whiteson, S. arXiv preprint arXiv:2202.00104. 2022.
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Improving Intrinsic Exploration with Language Abstractions. Mu, J.; Zhong, V.; Raileanu, R.; Jiang, M.; Goodman, N.; Rocktäschel, T.; and Grefenstette, E. In NeurIPS 2022, 2022.
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Evolving Curricula with Regret-Based Environment Design. Parker-Holder, J.; Jiang, M.; Dennis, M.; Samvelyan, M.; Foerster, J.; Grefenstette, E.; and Rocktäschel, T. In ICML 2022, 2022.
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SkillHack: A Benchmark for Skill Transfer in Open-Ended Reinforcement Learning. Matthews, M.; Samvelyan, M.; Parker-Holder, J.; Grefenstette, E.; and Rocktäschel, T. In ALOE 2022, 2022.
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A Study of Off-Policy Learning in Environments with Procedural Content Generation. Ehrenberg, A.; Kirk, R.; Jiang, M.; Grefenstette, E.; and Rocktäschel, T. In ALOE 2022, 2022.
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Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. Matthews, M.; Samvelyan, M.; Parker-Holder, J.; Grefenstette, E.; and Rocktäschel, T. In CoLLAs 2022, 2022.
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GriddlyJS: A Web IDE for Reinforcement Learning. Bamford, C.; Jiang, M.; Samvelyan, M.; and Rocktäschel, T. In NeurIPS 2022 (Datasets and Benchmarks), 2022.
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Dungeons and Data: A Large-Scale NetHack Dataset. Hambro, E.; Raileanu, R.; Rothermel, D.; Mella, V.; Rocktäschel, T.; Kuttler, H.; and Murray, N. In NeurIPS 2022 (Datasets and Benchmarks), 2022.
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Learning General World Models in a Handful of Reward-Free Deployments. Xu, Y.; Parker-Holder, J.; J. Ball, P.; Pacchiano, A.; Rybkin, O.; Roberts, S. J.; Rocktäschel, T.; and Grefenstette, E. In NeurIPS 2022, 2022.
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Exploration via Elliptical Episodic Bonuses. Henaff, M.; Raileanu, R.; Jiang, M.; and Rocktäschel, T. In NeurIPS 2022, 2022.
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Improving Policy Learning via Language Dynamics Distillation. Zhong, V.; Mu, J.; Zettlemoyer, L.; Grefenstette, E.; and Rocktäschel, T. In NeurIPS 2022, 2022.
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NeurIPS 2021 Competition and Demonstration Track Revised Selected Papers. Kiela, D.; Ciccone, M.; Caputo, B.; Kanervisto, A.; Milani, S.; Ramanauskas, K.; Topin, N.; Lin, Z.; Li, J.; Shi, J.; and others NeurIPS 2021 Competitions and Demonstrations Track. 2022.
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  2021 (14)
Learning with AMIGo: Adversarially Motivated Intrinsic Goals. Campero, A.; Raileanu, R.; Küttler, H.; Tenenbaum, J. B; Rocktäschel, T.; and Grefenstette, E. In ICLR 2021, 2021.
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KILT: a Benchmark for Knowledge Intensive Language Tasks. Petroni, F.; Piktus, A.; Fan, A.; Lewis, P.; Yazdani, M.; De Cao, N.; Thorne, J.; Jernite, Y.; Plachouras, V.; Rocktäschel, T.; and others In NAACL-HLT 2021, 2021.
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My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control. Kurin, V.; Igl, M.; Rocktäschel, T.; Boehmer, W.; and Whiteson, S. In ICLR 2021, 2021.
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How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds. Ammanabrolu, P.; Urbanek, J.; Li, M.; Szlam, A.; Rocktäschel, T.; and Weston, J. In NAACL-HLT 2021, 2021.
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Prioritized Level Replay. Jiang, M.; Grefenstette, E.; and Rocktäschel, T. In ICML 2021, 2021.
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Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning. Jiang, Z.; Minervini, P.; Jiang, M.; and Rocktäschel, T. In AAMAS 2021, 2021.
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MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. Samvelyan, M.; Kirk, R.; Kurin, V.; Parker-Holder, J.; Jiang, M.; Hambro, E.; Petroni, F.; Kuttler, H.; Grefenstette, E.; and Rocktäschel, T. In NeurIPS 2021 (Datasets and Benchmarks), 2021.
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Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers. Rothermel, D.; Li, M.; Rocktäschel, T.; and Foerster, J. In ICML 2021 Workshop: Self-Supervised Learning for Reasoning and Perception, 2021.
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Replay-Guided Adversarial Environment Design. Jiang, M.; Dennis, M. D; Parker-Holder, J.; Foerster, J. N.; Grefenstette, E.; and Rocktäschel, T. In NeurIPS 2021, 2021.
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Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay. Korshunova, I.; Jiang, M.; Parker-Holder, J.; Rocktäschel, T.; and Grefenstette, E. In DeepRL 2021, 2021.
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That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities. Parker-Holder, J.; Jiang, M.; Dennis, M. D; Samvelyan, M.; Nicolaus Foerster, J.; Grefenstette, E.; and Rocktäschel, T. In DeepRL 2021, 2021.
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Insights from the NeurIPS 2021 NetHack Challenge. Hambro, E.; Mohanty, S.; Babaev, D.; Byeon, M.; Chakraborty, D.; Grefenstette, E.; Jiang, M.; Jo, D.; Kanervisto, A.; Kim, J.; and others PMLR. 2021.
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Graph Backup: Data Efficient Backup Exploiting Markovian Transitions. Jiang, Z.; Zhang, T.; Kirk, R.; Rocktäschel, T.; and Grefenstette, E. In DeepRL 2021, 2021.
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Learning reasoning strategies in end-to-end differentiable proving. Minervini, P.; Riedel, S.; Stenetorp, P.; Grefenstette, E.; and Rocktäschel, T. In Neuro-Symbolic Artificial Intelligence: The State of the Art, pages 280–293. IOS Press, 2021.
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  2020 (11)
RTFM: Generalising to Novel Environment Dynamics via Reading. Zhong, V.; Rocktäschel, T.; and Grefenstette, E. In ICLR 2020, 2020.
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Differentiable Reasoning on Large Knowledge Bases and Natural Language. Minervini, P.; Bosnjak, M.; Rocktäschel, T.; Riedel, S.; and Grefenstette, E. In AAAI 2020 (𝗢𝗿𝗮𝗹), 2020.
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Generating Interactive Worlds with Text. Fan, A.; Urbanek, J.; Ringshia, P.; Dinan, E.; Qian, E.; Karamcheti, S.; Prabhumoye, S.; Kiela, D.; Rocktäschel, T.; Szlam, A.; and others In AAAI 2020, 2020.
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How Decoding Strategies Affect the Verifiability of Generated Text. Massarelli, L.; Petroni, F.; Piktus, A.; Ott, M.; Rocktäschel, T.; Plachouras, V.; Silvestri, F.; and Riedel, S. Findings of ACL: EMNLP 2020. 2020.
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RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments. Raileanu, R.; and Rocktäschel, T. In ICLR 2020, 2020.
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Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training. Stacey, J.; Minervini, P.; Dubossarsky, H.; Riedel, S.; and Rocktäschel, T. In EMNLP 2020, 2020.
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The NetHack Learning Environment. Küttler, H.; Nardelli, N.; Miller, A.; Raileanu, R.; Selvatici, M.; Grefenstette, E.; and Rocktäschel, T. In NeurIPS 2020, 2020.
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How Context Affects Language Models' Factual Predictions. Petroni, F.; Lewis, P.; Piktus, A.; Rocktäschel, T.; Wu, Y.; H. Miller, A.; and Riedel, S. In AKBC 2020 (𝗕𝗲𝘀𝘁 𝗣𝗮𝗽𝗲𝗿 𝗔𝘄𝗮𝗿𝗱), 2020.
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Retrieval-augmented Generation for Knowledge-intensive NLP Tasks. Lewis, P.; Perez, E.; Piktus, A.; Petroni, F.; Karpukhin, V.; Goyal, N.; Küttler, H.; Lewis, M.; Yih, W.; Rocktäschel, T.; and others In NeurIPS 2020, 2020.
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Learning Reasoning Strategies in End-to-End Differentiable Proving. Minervini, P.; Riedel, S.; Stenetorp, P.; Grefenstette, E.; and Rocktäschel, T. In ICML 2020, 2020.
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WordCraft: An Environment for Benchmarking Commonsense Agents. Jiang, M.; Luketina, J.; Nardelli, N.; Minervini, P.; Torr, P.; Whiteson, S.; and Rocktäschel, T. In LaReL 2020, 2020.
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  2019 (10)
Stable Opponent Shaping in Differentiable Games. Letcher, A.; Foerster, J.; Balduzzi, D.; Rocktäschel, T.; and Whiteson, S. In ICLR 2019, 2019.
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NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language. Weber, L.; Minervini, P.; Münchmeyer, J.; Leser, U.; and Rocktäschel, T. In ACL 2019, 2019.
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Learning to Speak and Act in a Fantasy Text Adventure Game. Urbanek, J.; Fan, A.; Karamcheti, S.; Jain, S.; Humeau, S.; Dinan, E.; Rocktäschel, T.; Kiela, D.; Szlam, A.; and Weston, J. In EMNLP 2019, 2019.
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A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. Mao, J.; Foerster, J.; Rocktäschel, T.; Al-Shedivat, M.; Farquhar, G.; and Whiteson, S. In ICML 2019, 2019.
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A Survey of Reinforcement Learning Informed by Natural Language. Luketina, J; Nardelli, N; Farquhar, G; Foerster, J; Andreas, J; Grefenstette, E; Whiteson, S; and Rocktäschel, T In IJCAI 2019, 2019.
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HUNER: Improving Biomedical NER with Pretraining. Weber, L.; Münchmeyer, J.; Rocktäschel, T.; Habibi, M.; and Leser, U. Bioinformatics. 2019.
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Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings. Cowen-Rivers, A. I; Minervini, P.; Rocktaschel, T.; Bosnjak, M.; Riedel, S.; and Wang, J. NeSy 2019. 2019.
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Language Models as Knowledge Bases?. Petroni, F.; Rocktäschel, T.; Lewis, P.; Bakhtin, A.; Wu, Y.; Miller, A. H; and Riedel, S. In EMNLP 2019, 2019.
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MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions. Sivakumar, V.; Delalleau, O.; Rocktäschel, T.; Miller, A. H.; Küttler, H.; Nardelli, N.; Rabbat, M.; Pineau, J.; and Riedel, S. In ML for Systems 2019, 2019.
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TorchBeast: A PyTorch Platform for Distributed RL. Küttler, H.; Nardelli, N.; Lavril, T.; Selvatici, M.; Sivakumar, V.; Rocktäschel, T.; and Grefenstette, E. 2019.
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  2018 (7)
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning. Farquhar, G.; Rocktäschel, T.; Igl, M.; and Whiteson, S. In ICLR 2018, 2018.
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DiCE: The Infinitely Differentiable Monte-Carlo Estimator. Foerster, J.; Farquhar, G.; Al-Shedivat, M.; Rocktäschel, T.; Xing, E. P; and Whiteson, S. In ICML 2018, 2018.
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Jack the Reader-A Machine Reading Framework. Weissenborn, D.; Minervini, P.; Dettmers, T.; Augenstein, I.; Welbl, J.; Rocktäschel, T.; Bošnjak, M.; Mitchell, J.; Demeester, T.; Stenetorp, P.; and others In ACL 2018 Demo, 2018.
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Interpretation of Natural Language Rules in Conversational Machine Reading. Saeidi, M.; Bartolo, M.; Lewis, P.; Singh, S.; Rocktäschel, T.; Sheldon, M.; Bouchard, G.; and Riedel, S. In EMNLP 2018, 2018.
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e-SNLI: Natural Language Inference with Natural Language Explanations. Camburu, O.; Rocktäschel, T.; Lukasiewicz, T.; and Blunsom, P. In NeurIPS 2018, 2018.
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Towards Neural Theorem Proving at Scale. Minervini, P.; Bosnjak, M.; Rocktäschel, T.; and Riedel, S. In NAMPI 2018, 2018.
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Einsum is All you Need - Einstein Summation in Deep Learning. Rocktäschel, T. 2018.
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  2017 (5)
Programming with a Differentiable Forth Interpreter. Bošnjak, M.; Rocktäschel, T.; Naradowsky, J.; and Riedel, S. In ICML 2017, 2017.
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Frustratingly Short Attention Spans in Neural Language Modeling. Daniluk, M.; Rocktäschel, T.; Welbl, J.; and Riedel, S. In ICLR 2017, 2017.
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End-to-End Differentiable Proving. Rocktäschel, T.; and Riedel, S. In NeurIPS 2017 (𝗢𝗿𝗮𝗹), 2017.
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Adversarial Sets for Regularising Neural Link Predictors. Minervini, P.; Demeester, T.; Rocktäschel, T.; and Riedel, S. In UAI 2017, 2017.
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Combining Representation Learning with Logic for Language Processing. Rocktäschel, T. 2017.
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  2016 (10)
Reasoning about Entailment with Neural Attention. Rocktäschel, T.; Grefenstette, E.; Hermann, K. M.; Kočiskỳ, Tomáš; and Blunsom, P. In ICLR 2016, 2016.
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Learning Knowledge Base Inference with Neural Theorem Provers. Rocktäschel, T.; and Riedel, S. In AKBC 2016, 2016.
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Regularizing Relation Representations by First-order Implications. Demeester, T.; Rocktäschel, T.; and Riedel, S. In AKBC 2016, 2016.
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SETH Detects and Normalizes Genetic Variants in Text. Thomas, P.; Rocktäschel, T.; Hakenberg, J.; Lichtblau, Y.; and Leser, U. Bioinformatics. 2016.
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Generating Natural Language Inference Chains. Kolesnyk, V.; Rocktäschel, T.; and Riedel, S. Technical Report arXiv:1606.01404. 2016.
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MuFuRU: The Multi-Function Recurrent Unit. Weissenborn, D.; and Rocktäschel, T. In RepL4NLP 2016, 2016.
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Stance Detection with Bidirectional Conditional Encoding. Augenstein, I.; Rocktäschel, T.; Vlachos, A.; and Bontcheva, K. In EMNLP 2016, 2016.
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Lifted Rule Injection for Relation Embeddings. Demeester, T.; Rocktäschel, T.; and Riedel, S. In EMNLP 2016, 2016.
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emoji2vec: Learning Emoji Representations from their Description. Eisner, B.; Rocktäschel, T.; Augenstein, I.; Bošnjak, M.; and Riedel, S. 2016.
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Learning Python Code Suggestion with a Sparse Pointer Network. Bhoopchand, A.; Rocktäschel, T.; Barr, E.; and Riedel, S. 2016.
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  2015 (7)
The CHEMDNER Corpus of Chemicals and Drugs and its Annotation Principles. Krallinger, M.; Rabal, O.; Leitner, F.; Vazquez, M.; Salgado, D.; Lu, Z.; Leaman, R.; Lu, Y.; Ji, D.; Lowe, D. M; and others Journal of Cheminformatics. 2015.
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Towards Extracting Faithful and Descriptive Representations of Latent Variable Models. Sánchez, I.; Rocktäschel, T.; Riedel, S.; and Singh, S. KRR 2015. 2015.
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Injecting Logical Background Knowledge into Embeddings for Relation Extraction. Rocktäschel, T.; Singh, S.; and Riedel, S. In NAACL 2015, 2015.
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Matrix and Tensor Factorization Methods for Natural Language Processing. Bouchard, G.; Naradowsky, J.; Riedel, S.; Rocktäschel, T.; and Vlachos, A. 2015.
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WOLFE: An NLP-friendly Declarative Machine Learning Stack. Singh, S.; Rocktäschel, T.; Hewitt, L.; Naradowsky, J.; and Riedel, S. In NAACL 2015 Demo, 2015.
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Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction. Singh, S.; Rocktäschel, T.; and Riedel, S. In VSM 2015, 2015.
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Towards Two-way Interaction with Reading Machines. Riedel, S.; Singh, S.; Bouchard, G.; Rocktäschel, T.; and Sanchez, I. In SLSP 2015, 2015.
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  2014 (3)
Low-Dimensional Embeddings of Logic. Rocktäschel, T.; Bosnjak, M.; Singh, S.; and Riedel, S. In SP 2014 (𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗔𝘄𝗮𝗿𝗱), 2014.
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WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming. Riedel, S. R.; Singh, S.; Srikumar, V.; Rocktäschel, T.; Visengeriyeva, L.; and Noessner, J. In StarAI 2014, 2014.
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Designing an IDE for Probabilistic Programming: Challenges and a Prototype. Singh, S.; Riedel, S.; Hewitt, L.; and Rocktäschel, T. In ProbProg 2014, 2014.
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  2013 (3)
WBI-DDI: Drug-drug Interaction Extraction using Majority Voting. Thomas, P.; Neves, M.; Rocktäschel, T.; and Leser, U. In SemEval 2013, 2013.
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WBI-NER: The Impact of Domain-specific Features on the Performance of Identifying and Classifying Mentions of Drugs. Rocktäschel, T.; Huber, T.; Weidlich, M.; and Leser, U. SemEval 2013. 2013.
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Extended Feature Set for Chemical Named Entity Recognition and Indexing. Huber, T.; Rocktäschel, T.; Weidlich, M.; Thomas, P.; and Leser, U. BioCreative 2013. 2013.
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  2012 (1)
ChemSpot: a Hybrid System for Chemical Named Entity Recognition. Rocktäschel, T.; Weidlich, M.; and Leser, U. Bioinformatics. 2012.
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