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ORCIDLuisa M. Zintgraf
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Journal Articles
- 2022
[j3]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:
A practical guide to multi-objective reinforcement learning and planning. Auton. Agents Multi Agent Syst. 36(1): 26 (2022)- 2021
- [j2]Luisa M. Zintgraf, Sebastian Schulze, Cong Lu, Leo Feng, Maximilian Igl, Kyriacos Shiarlis, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning. J. Mach. Learn. Res. 22: 289:1-289:39 (2021) - 2017
- [j1]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:
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinform. 18(1): 441:1-441:13 (2017)
Conference and Workshop Papers
- 2023
- [c15]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:
A Brief Guide to Multi-Objective Reinforcement Learning and Planning. AAMAS 2023: 1988-1990 - 2022
- [c14]Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal:
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. ICLR 2022 - [c13]Darius Muglich, Luisa M. Zintgraf, Christian A. Schröder de Witt, Shimon Whiteson, Jakob N. Foerster:
Generalized Beliefs for Cooperative AI. ICML 2022: 16062-16082 - [c12]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:
Communicating via Markov Decision Processes. ICML 2022: 20314-20328 - 2021
- [c11]Lida Theodorou, Daniela Massiceti, Luisa M. Zintgraf, Simone Stumpf, Cecily Morrison, Edward Cutrell, Matthew Tobias Harris, Katja Hofmann:
Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors. ASSETS 2021: 27:1-27:12 - [c10]Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann:
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. AAMAS 2021: 1712-1714 - [c9]Daniela Massiceti, Luisa M. Zintgraf, John Bronskill, Lida Theodorou, Matthew Tobias Harris, Edward Cutrell, Cecily Morrison, Katja Hofmann, Simone Stumpf:
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition. ICCV 2021: 10798-10808 - [c8]Luisa M. Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson:
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. ICML 2021: 12991-13001 - 2020
- [c7]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. ICLR 2020 - [c6]Diederik M. Roijers, Luisa M. Zintgraf, Pieter Libin, Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé:
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models. ECML/PKDD (3) 2020: 463-478 - 2019
- [c5]Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson:
Fast Context Adaptation via Meta-Learning. ICML 2019: 7693-7702 - 2018
- [c4]Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé:
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making. AAMAS 2018: 1477-1485 - [c3]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. ICML 2018: 2122-2131 - 2017
- [c2]Diederik M. Roijers, Luisa M. Zintgraf, Ann Nowé:
Interactive Thompson Sampling for Multi-objective Multi-armed Bandits. ADT 2017: 18-34 - [c1]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ICLR (Poster) 2017
Informal and Other Publications
- 2023
- [i18]Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa M. Zintgraf, Chelsea Finn, Shimon Whiteson:
A Survey of Meta-Reinforcement Learning. CoRR abs/2301.08028 (2023) - 2022
- [i17]Milad Alizadeh, Shyam A. Tailor, Luisa M. Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal:
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients. CoRR abs/2202.08132 (2022) - [i16]Darius Muglich, Luisa M. Zintgraf, Christian Schröder de Witt, Shimon Whiteson, Jakob N. Foerster:
Generalized Beliefs for Cooperative AI. CoRR abs/2206.12765 (2022) - 2021
- [i15]Luisa M. Zintgraf, Sam Devlin, Kamil Ciosek, Shimon Whiteson, Katja Hofmann:
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning. CoRR abs/2101.03864 (2021) - [i14]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:
A Practical Guide to Multi-Objective Reinforcement Learning and Planning. CoRR abs/2103.09568 (2021) - [i13]Daniela Massiceti, Luisa M. Zintgraf, John Bronskill, Lida Theodorou, Matthew Tobias Harris, Edward Cutrell, Cecily Morrison, Katja Hofmann, Simone Stumpf:
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition. CoRR abs/2104.03841 (2021) - [i12]Eltayeb Ahmed, Luisa M. Zintgraf, Christian A. Schröder de Witt, Nicolas Usunier:
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings. CoRR abs/2104.08492 (2021) - [i11]Alexandra Moringen, Sören Rüttgers, Luisa M. Zintgraf, Jason Friedman, Helge J. Ritter:
Optimizing piano practice with a utility-based scaffold. CoRR abs/2106.12937 (2021) - [i10]Samuel Sokota, Christian Schröder de Witt, Maximilian Igl, Luisa M. Zintgraf, Philip H. S. Torr, Shimon Whiteson, Jakob N. Foerster:
Implicit Communication as Minimum Entropy Coupling. CoRR abs/2107.08295 (2021) - [i9]Zheng Xiong, Luisa M. Zintgraf, Jacob Beck, Risto Vuorio, Shimon Whiteson:
On the Practical Consistency of Meta-Reinforcement Learning Algorithms. CoRR abs/2112.00478 (2021) - 2020
- [i8]Luisa M. Zintgraf, Leo Feng, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson:
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning. CoRR abs/2010.01062 (2020) - 2019
- [i7]Luisa M. Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson:
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning. CoRR abs/1910.08348 (2019) - [i6]Leo Feng, Luisa M. Zintgraf, Bei Peng, Shimon Whiteson:
VIABLE: Fast Adaptation via Backpropagating Learned Loss. CoRR abs/1911.13159 (2019) - 2018
- [i5]Luisa M. Zintgraf, Diederik M. Roijers, Sjoerd Linders, Catholijn M. Jonker, Ann Nowé:
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making. CoRR abs/1802.07606 (2018) - [i4]Maximilian Igl, Luisa M. Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson:
Deep Variational Reinforcement Learning for POMDPs. CoRR abs/1806.02426 (2018) - [i3]Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson:
CAML: Fast Context Adaptation via Meta-Learning. CoRR abs/1810.03642 (2018) - 2017
- [i2]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. CoRR abs/1702.04595 (2017) - 2016
- [i1]Luisa M. Zintgraf, Taco S. Cohen, Max Welling:
A New Method to Visualize Deep Neural Networks. CoRR abs/1603.02518 (2016)
Coauthor Index
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