Luisa M Zintgraf
Luisa M Zintgraf
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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
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
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
Interactive thompson sampling for multi-objective multi-armed bandits
DM Roijers, LM Zintgraf, A Nowé
International Conference on Algorithmic DecisionTheory, 18-34, 2017
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), 1-13, 2017
A practical guide to multi-objective reinforcement learning and planning
CF Hayes, R Rădulescu, E Bargiacchi, J Källström, M Macfarlane, ...
arXiv preprint arXiv:2103.09568, 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
Variational Task Embeddings for Fast Adaptation in Deep Reinforcement Learning
L Zintgraf, M Igl, K Shiarlis, A Mahajan, K Hofmann, S Whiteson
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
ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
D Massiceti, L Zintgraf, J Bronskill, L Theodorou, MT Harris, E Cutrell, ...
arXiv preprint arXiv:2104.03841, 2021
MultiMAuS: A Multi-Modal Authentication Simulator for Fraud Detection Research
LM Zintgraf, EA Lopez-Rojas, DM Roijers, A Nowé
The European Modeling and Simulation Symposium (EMSS) 2017, 2017
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
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
L Zintgraf, S Devlin, K Ciosek, S Whiteson, K Hofmann
AAMAS 2021 (Extended Abstract), 2021
Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models.
DM Roijers, LM Zintgraf, P Libin, M Reymond, E Bargiacchi, A Nowé
ECML/PKDD (3), 463-478, 2020
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, ...
The 23rd International ACM SIGACCESS Conference on Computers and …, 2021
Implicit Communication as Minimum Entropy Coupling
S Sokota, CS de Witt, M Igl, L Zintgraf, P Torr, S Whiteson, J Foerster
arXiv preprint arXiv:2107.08295, 2021
A Self-Supervised Auxiliary Loss for Deep RL in Partially Observable Settings
E Ahmed, L Zintgraf, CAS de Witt, N Usunier
arXiv preprint arXiv:2104.08492, 2021
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