Maximilian Igl
TitleCited byYear
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
612017
Auto-encoding sequential monte carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
arXiv preprint arXiv:1705.10306, 2017
612017
Tighter variational bounds are not necessarily better
T Rainforth, AR Kosiorek, TA Le, CJ Maddison, M Igl, F Wood, YW Teh
arXiv preprint arXiv:1802.04537, 2018
582018
Deep variational reinforcement learning for pomdps
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
arXiv preprint arXiv:1806.02426, 2018
362018
Treeqn and atreec: Differentiable tree planning for deep reinforcement learning
G Farquhar, T Rocktäschel, M Igl, SA Whiteson
International Conference on Learning Representations, 2018
272018
Treeqn and atreec: Differentiable tree-structured models for deep reinforcement learning
G Farquhar, T Rocktäschel, M Igl, S Whiteson
arXiv preprint arXiv:1710.11417, 2017
82017
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
arXiv preprint arXiv:1910.08348, 2019
2019
Multitask Soft Option Learning
M Igl, A Gambardella, N Nardelli, N Siddharth, W Böhmer, S Whiteson
arXiv preprint arXiv:1904.01033, 2019
2019
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
Advances in Neural Information Processing Systems, 13956-13968, 2019
2019
VARIATIONAL TASK EMBEDDINGS FOR FAST ADAPTA-TION IN DEEP REINFORCEMENT LEARNING
L Zintgraf, M Igl, K Shiarlis, A Mahajan, K Hofmann, S Whiteson
International Conference on Learning Representations Workshop on Structure …, 2019
2019
Composition of queries in probabilistic programming languages
M Igl
2016
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Articles 1–11