A New Method to Visualize Deep Neural Networks. - Luisa M. Zintgraf; Taco S. Cohen; Max Welling (CoRR, 2016)

Interactive Thompson Sampling for Multi-objective Multi-armed Bandits. - Diederik M. Roijers; Luisa M. Zintgraf; Ann Nowé (ADT, 2017)

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. - Luisa M. Zintgraf; Taco S. Cohen; Tameem Adel; Max Welling (ICLR (Poster), 2017)

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. - A. Eck; Luisa M. Zintgraf; E. F. J. de Groot; Tim G. J. de Meij; Taco S. Cohen; P. H. M. Savelkoul; Max Welling; A. E. Budding (BMC Bioinform., 2017)

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making. - Luisa M. Zintgraf; Diederik M. Roijers; Sjoerd Linders; Catholijn M. Jonker; Ann Nowé (AAMAS, 2018)

Deep Variational Reinforcement Learning for POMDPs. - Maximilian Igl; Luisa M. Zintgraf; Tuan Anh Le 001; Frank Wood; Shimon Whiteson (ICML, 2018)

CAML: Fast Context Adaptation via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Vitaly Kurin; Katja Hofmann; Shimon Whiteson (CoRR, 2018)

Fast Context Adaptation via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Vitaly Kurin; Katja Hofmann; Shimon Whiteson (ICML, 2019)

VIABLE: Fast Adaptation via Backpropagating Learned Loss. - Leo Feng; Luisa M. Zintgraf; Bei Peng 001; Shimon Whiteson (CoRR, 2019)

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Kyriacos Shiarlis; Maximilian Igl; Sebastian Schulze; Yarin Gal; Katja Hofmann; Shimon Whiteson (ICLR, 2020)

Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. - Diederik M. Roijers; Luisa M. Zintgraf; Pieter Libin; Mathieu Reymond; Eugenio Bargiacchi; Ann Nowé (ECML/PKDD (3), 2020)

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. - Luisa M. Zintgraf; Leo Feng; Maximilian Igl; Kristian Hartikainen; Katja Hofmann; Shimon Whiteson (CoRR, 2020)

Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors. - Lida Theodorou; Daniela Massiceti; Luisa M. Zintgraf; Simone Stumpf; Cecily Morrison; Edward Cutrell; Matthew Tobias Harris; Katja Hofmann (ASSETS, 2021)

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. - Luisa M. Zintgraf; Sam Devlin; Kamil Ciosek; Shimon Whiteson; Katja Hofmann (AAMAS, 2021)

ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition. - Daniela Massiceti; Luisa M. Zintgraf; John Bronskill; Lida Theodorou; Matthew Tobias Harris; Edward Cutrell; Cecily Morrison; Katja Hofmann; Simone Stumpf (ICCV, 2021)

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. - Luisa M. Zintgraf; Leo Feng; Cong Lu; Maximilian Igl; Kristian Hartikainen; Katja Hofmann; Shimon Whiteson (ICML, 2021)

A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings. - Eltayeb Ahmed; Luisa M. Zintgraf; Christian A. Schröder de Witt; Nicolas Usunier (CoRR, 2021)

Optimizing piano practice with a utility-based scaffold. - Alexandra Moringen; Sören Rüttgers; Luisa M. Zintgraf; Jason Friedman 001; Helge J. Ritter (CoRR, 2021)

Implicit Communication as Minimum Entropy Coupling. - Samuel Sokota; Christian Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Shimon Whiteson; Jakob N. Foerster (CoRR, 2021)

On the Practical Consistency of Meta-Reinforcement Learning Algorithms. - Zheng Xiong; Luisa M. Zintgraf; Jacob Beck; Risto Vuorio; Shimon Whiteson (CoRR, 2021)

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. - Luisa M. Zintgraf; Sebastian Schulze; Cong Lu; Leo Feng; Maximilian Igl; Kyriacos Shiarlis; Yarin Gal; Katja Hofmann; Shimon Whiteson (J. Mach. Learn. Res., 2021)

Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. - Milad Alizadeh; Shyam A. Tailor; Luisa M. Zintgraf; Joost van Amersfoort; Sebastian Farquhar; Nicholas Donald Lane; Yarin Gal (ICLR, 2022)

Generalized Beliefs for Cooperative AI. - Darius Muglich; Luisa M. Zintgraf; Christian A. Schröder de Witt; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

Communicating via Markov Decision Processes. - Samuel Sokota; Christian A. Schröder de Witt; Maximilian Igl; Luisa M. Zintgraf; Philip H. S. Torr; Martin Strohmeier; J. Zico Kolter; Shimon Whiteson; Jakob N. Foerster (ICML, 2022)

A practical guide to multi-objective reinforcement learning and planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (Auton. Agents Multi Agent Syst., 2022)

A Brief Guide to Multi-Objective Reinforcement Learning and Planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (AAMAS, 2023)

A Survey of Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (CoRR, 2023)

A Tutorial on Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (Found. Trends Mach. Learn., 2025)

Scalable Meta-Learning via Mixed-Mode Differentiation. - Iurii Kemaev; Dan A. Calian; Luisa M. Zintgraf; Gregory Farquhar; Hado van Hasselt (CoRR, 2025)

DataRater: Meta-Learned Dataset Curation. - Dan A. Calian; Gregory Farquhar; Iurii Kemaev; Luisa M. Zintgraf; Matteo Hessel; Jeremy Shar; Junhyuk Oh; András György 001; Tom Schaul; Jeffrey Dean; Hado van Hasselt; David Silver (CoRR, 2025)