Luisa M. Zintgraf

According to our database, Luisa M. Zintgraf authored at least 27 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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 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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

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, Patrick Mannion, Ann Nowé, Gabriel de Oliveira Ramos, Marcello Restelli, Peter Vamplew, Diederik M. Roijers

Auton. Agents Multi Agent Syst., 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

Proceedings of the International Conference on Machine Learning, 2022

Generalized Beliefs for Cooperative AI.

Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster

Proceedings of the International Conference on Machine Learning, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

On the Practical Consistency of Meta-Reinforcement Learning Algorithms.

Zheng Xiong, Luisa M. Zintgraf, Jacob Beck, Risto Vuorio, Shimon Whiteson

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

Optimizing piano practice with a utility-based scaffold.

Alexandra Moringen, Sören Rüttgers, Luisa M. Zintgraf, Jason Friedman, Helge J. Ritter

CoRR, 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

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

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning.

Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

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

Proceedings of the ASSETS '21: The 23rd International ACM SIGACCESS Conference on Computers and Accessibility, 2021

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

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

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

VIABLE: Fast Adaptation via Backpropagating Learned Loss.

Leo Feng, Luisa M. Zintgraf, Bei Peng, Shimon Whiteson

CoRR, 2019

Fast Context Adaptation via Meta-Learning.

Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

CAML: Fast Context Adaptation via Meta-Learning.

Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson

CoRR, 2018

Deep Variational Reinforcement Learning for POMDPs.

Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

Proceedings of the 35th International Conference on Machine Learning, 2018

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making.

Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

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

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis.

Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling

Proceedings of the 5th International Conference on Learning Representations, 2017

Interactive Thompson Sampling for Multi-objective Multi-armed Bandits.

Diederik M. Roijers, Luisa M. Zintgraf, Ann Nowé

Proceedings of the Algorithmic Decision Theory - 5th International Conference, 2017

2016

A New Method to Visualize Deep Neural Networks.

Luisa M. Zintgraf, Taco S. Cohen, Max Welling

CoRR, 2016