Sotabase

Career

· Senior Research Scientist, NVIDIA2020–
· Research Intern, Deepmind2019–2019
· Research Intern, Microsoft Research2019–2019
· DPhil in Autonomous Intelligent Machines and Systems, University of Oxford2015–
· Consulting Director, 180 Degrees Consulting Munich2014–2015
· Werkstudent, PreisAnalytics GmbH2013–2014

Publications (29)

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
International Conference on Learning Representations · 2019
306
cited
Deep Variational Reinforcement Learning for POMDPs
International Conference on Machine Learning · 2018
288
cited
Tighter Variational Bounds are Not Necessarily Better
International Conference on Machine Learning · 2018
208
cited
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Neural Information Processing Systems · 2019
189
cited
Auto-Encoding Sequential Monte Carlo
International Conference on Learning Representations · 2017
159
cited
My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
International Conference on Learning Representations · 2020
101
cited
Transient Non-stationarity and Generalisation in Deep Reinforcement Learning
International Conference on Learning Representations · 2020
98
cited
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
IEEE International Conference on Robotics and Automation · 2022
80
cited
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
International Conference on Learning Representations · 2017
72
cited
TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning
International Conference on Learning Representations · 2017
71
cited
VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning
Journal of machine learning research · 2021
58
cited
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
International Conference on Machine Learning · 2020
43
cited
The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning
arXiv.org · 2020
35
cited
Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
Computer Vision and Pattern Recognition · 2024
32
cited
Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-Tuning
IEEE International Conference on Robotics and Automation · 2024
31
cited
Multitask Soft Option Learning
Conference on Uncertainty in Artificial Intelligence · 2019
26
cited
STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
arXiv.org · 2024
25
cited
Communicating via Markov Decision Processes
International Conference on Machine Learning · 2021
14
cited
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Neural Information Processing Systems · 2021
14
cited
Hierarchical Imitation Learning for Stochastic Environments
IEEE/RJS International Conference on Intelligent RObots and Systems · 2023
3
cited
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Maximilian Igl | Researcher Profile | Sotabase | Sotabase