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Rising Stars
2025

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Rising Stars

D-Index
42
Citations
10935
World Ranking
547
National Ranking
24

Computer Science

D-Index
43
Citations
12127
World Ranking
7796
National Ranking
466

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Tim Rocktäschel is affiliated with University College London in the United Kingdom. Their research primarily spans the field of Computer Science, with significant contributions in Artificial Intelligence, which accounts for the majority of their publications.

Their work also crosses into related subfields such as Management Science and Operations Research, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, and Sociology and Political Science.

Key topics addressed in their research include:

  • Reinforcement Learning in Robotics
  • Topic Modeling
  • Natural Language Processing Techniques
  • Data Stream Mining Techniques
  • Adversarial Robustness in Machine Learning
  • Artificial Intelligence in Games
  • Explainable Artificial Intelligence (XAI)

Among Tim Rocktäschel's recent publications are:

  • "Contextual Personal Intelligence: A New Paradigm for AI That Evolves With You," 2025, arXiv (Cornell University)
  • "A Survey of Zero-shot Generalisation in Deep Reinforcement Learning," 2023, Journal of Artificial Intelligence Research
  • "How Context Affects Language Models' Factual Predictions," 2020, arXiv (Cornell University)
  • "Differentiable Reasoning on Large Knowledge Bases and Natural Language," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Learning with AMIGo: Adversarially Motivated Intrinsic Goals," 2020, arXiv (Cornell University)

Their research has been featured predominantly in venues including:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Artificial Intelligence Research
  • Royal Society Open Science

Frequent collaborators in their research network include Edward Grefenstette, Minqi Jiang, Jack Parker-Holder, Mikayel Samvelyan, and Jakob Foerster.

Best Publications

  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

    Patrick S. H. Lewis;Ethan Perez;Aleksandra Piktus;Fabio Petroni

  • Language Models as Knowledge Bases

    Fabio Petroni;Tim Rocktäschel;Patrick S. H. Lewis;Anton Bakhtin

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Ko iský;Tomáš Ko iský

  • Reasoning about Entailment with Neural Attention

    Tim Rocktäschel;Edward Grefenstette;Karl Moritz Hermann;Tomáš Kočiský

  • e-SNLI: Natural Language Inference with Natural Language Explanations

    Oana-Maria Camburu;Tim Rocktäschel;Thomas Lukasiewicz;Phil Blunsom

  • Stance detection with bidirectional conditional encoding

    Isabelle Augenstein;Tim Rocktäschel;Andreas Vlachos;Kalina Bontcheva

  • The CHEMDNER corpus of chemicals and drugs and its annotation principles.

    Martin Krallinger;Obdulia Rabal;Florian Leitner;Miguel Vazquez

  • ChemSpot: a hybrid system for chemical named entity recognition

    Tim Rocktäschel;Michael Weidlich;Ulf Leser

  • emoji2vec: Learning Emoji Representations from their Description

    Ben Eisner;Tim Rocktäschel;Isabelle Augenstein;Matko Bosnjak

  • Injecting Logical Background Knowledge into Embeddings for Relation Extraction

    Tim Rocktäschel;Sameer Singh;Sebastian Riedel

  • KILT: a Benchmark for Knowledge Intensive Language Tasks

    Fabio Petroni;Aleksandra Piktus;Angela Fan;Patrick S. H. Lewis

  • End-to-end differentiable proving

    Tim Rocktäschel;Sebastian Riedel

  • A Survey of Reinforcement Learning Informed by Natural Language

    Jelena Luketina;Nantas Nardelli;Nantas Nardelli;Gregory Farquhar;Gregory Farquhar;Jakob N. Foerster

  • A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

    Unknown

  • Interpretation of Natural Language Rules in Conversational Machine Reading

    Marzieh Saeidi;Max Bartolo;Patrick S. H. Lewis;Sameer Singh

  • Lifted Rule Injection for Relation Embeddings

    Thomas Demeester;Tim Rocktäschel;Sebastian Riedel

  • Learning to Speak and Act in a Fantasy Text Adventure Game

    Jack Urbanek;Angela Fan;Siddharth Karamcheti;Saachi Jain

  • How Context Affects Language Models' Factual Predictions

    Fabio Petroni;Patrick S. H. Lewis;Aleksandra Piktus;Tim Rocktäschel

  • Programming with a differentiable forth interpreter

    Matko Bošnjak;Tim Rocktäschel;Jason Naradowsky;Sebastian Riedel

  • Frustratingly short attention spans in neural language modeling

    Michal Daniluk;Tim Rocktäschel;Johannes Welbl;Sebastian Riedel

  • RIDE: Rewarding Impact-Driven Exploration for Procedurally-Generated Environments

    Roberta Raileanu;Tim Rocktäschel

  • The NetHack Learning Environment

    Heinrich Küttler;Nantas Nardelli;Alexander H. Miller;Roberta Raileanu

Frequent Co-Authors

Sebastian Riedel
Sebastian Riedel University College London
Edward Grefenstette
Edward Grefenstette University College London
Shimon Whiteson
Shimon Whiteson University of Oxford
Jakob Foerster
Jakob Foerster University of Oxford
Ulf Leser
Ulf Leser Humboldt-Universität zu Berlin
Sameer Singh
Sameer Singh University of California, Irvine
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Jason Weston
Jason Weston Facebook (United States)
Isabelle Augenstein
Isabelle Augenstein University of Copenhagen
Douwe Kiela
Douwe Kiela Stanford University

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