Luisa Zintgraf
Luisa Zintgraf
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Cited by
Visualizing deep neural network decisions: Prediction difference analysis
LM Zintgraf, TS Cohen, T Adel, M Welling
The fifth International Conference on Learning Representations (ICLR 2017), 2017
Fast Context Adaptation via Meta-Learning
LM Zintgraf, K Shiarlis, V Kurin, K Hofmann, S Whiteson
Thirty-sixth International Conference on Machine Learning (ICML 2019), 2018
Deep Variational Reinforcement Learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
Thirty-fifth International Conference on Machine Learning (ICML 2018), 2018
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson
International Conference on Learning Representations (ICLR) 2020, 2019
A practical guide to multi-objective reinforcement learning and planning
CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ...
Autonomous Agents and Multi-Agent Systems 36 (1), 26, 2022
A survey of meta-reinforcement learning
J Beck, R Vuorio, EZ Liu, Z Xiong, L Zintgraf, C Finn, S Whiteson
arXiv preprint arXiv:2301.08028, 2023
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
LM Zintgraf, DM Roijers, S Linders, CM Jonker, A Nowé
17th International Conference on Autonomous Agents and Multiagent Systems …, 2018
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
L Zintgraf, L Feng, C Lu, M Igl, K Hartikainen, K Hofmann, S Whiteson
International Conference on Machine Learning (ICML) 2021, 2020
Orbit: A real-world few-shot dataset for teachable object recognition
D Massiceti, L Zintgraf, J Bronskill, L Theodorou, MT Harris, E Cutrell, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021
Quality assessment of MORL algorithms: A utility-based approach
LM Zintgraf, TV Kanters, DM Roijers, F Oliehoek, P Beau
Benelearn 2015: proceedings of the 24th annual machine learning conference …, 2015
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
L Zintgraf, S Devlin, K Ciosek, S Whiteson, K Hofmann
AAMAS 2021 (Extended Abstract), 2021
Interactive thompson sampling for multi-objective multi-armed bandits
DM Roijers, LM Zintgraf, A Nowé
Algorithmic Decision Theory: 5th International Conference, ADT 2017 …, 2017
Varibad: Variational bayes-adaptive deep rl via meta-learning
L Zintgraf, S Schulze, C Lu, L Feng, M Igl, K Shiarlis, Y Gal, K Hofmann, ...
Journal of Machine Learning Research 22 (289), 1-39, 2021
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions
A Eck, LM Zintgraf, EFJ de Groot, TGJ de Meij, TS Cohen, PHM Savelkoul, ...
BMC bioinformatics 18, 1-13, 2017
Caml: Fast context adaptation via meta-learning
LM Zintgraf, K Shiarlis, V Kurin, K Hofmann, S Whiteson
Prospect pruning: Finding trainable weights at initialization using meta-gradients
M Alizadeh, SA Tailor, LM Zintgraf, J van Amersfoort, S Farquhar, ...
arXiv preprint arXiv:2202.08132, 2022
Disability-first dataset creation: Lessons from constructing a dataset for teachable object recognition with blind and low vision data collectors
L Theodorou, D Massiceti, L Zintgraf, S Stumpf, C Morrison, E Cutrell, ...
Proceedings of the 23rd International ACM SIGACCESS Conference on Computers …, 2021
Interactive multi-objective reinforcement learning in multi-armed bandits for any utility function
DM Roijers, LM Zintgraf, P Libin, A Nowé
ALA workshop at FAIM 8, 2018
MORL-Glue: A benchmark suite for multi-objective reinforcement learning
P Vamplew, D Webb, LM Zintgraf, DM Roijers, R Dazeley, R Issabekov, ...
29th Benelux Conference on Artificial Intelligence November 8–9, 2017 …, 2017
On the practical consistency of meta-reinforcement learning algorithms
Z Xiong, L Zintgraf, J Beck, R Vuorio, S Whiteson
arXiv preprint arXiv:2112.00478, 2021
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