Lenart Treven | Learning & Adaptive Systems Group

Lenart Treven

Ph.D. Student
External Website
lenart.treven@inf.ethz.ch
CAB F 61.1
+41 77 999 6574
While my research interest is very broad, I am currently exploring the areas of Continuous-time Reinforcement Learning.

Publications

2024
NeoRL: Efficient Exploration for Nonepisodic RL B. Sukhija, L. Treven, F. Dörfler, S. Coros, A. Krause In Advances in Neural Information Processing Systems (NeurIPS), 2024 Spotlight [bibtex] [abstract] [pdf]
When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL L. Treven, B. Sukhija, Y. As, F. Dörfler, A. Krause In Advances in Neural Information Processing Systems (NeurIPS), 2024 [bibtex] [abstract] [pdf]
Transductive Active Learning: Theory and Applications J. Hübotter, B. Sukhija, L. Treven, Y. As, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2024 Oral presentation at Aligning Reinforcement Learning Experimentalists and Theorists Workshop at ICML [bibtex] [abstract] [pdf]
Bridging the Sim-to-Real Gap with Bayesian Inference J. Rothfuss, B. Sukhija, L. Treven, F. Dörfler, S. Coros, A. Krause In Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS), 2024 Oral presentation* [bibtex] [abstract] [pdf]
2023
Optimistic Active Exploration of Dynamical Systems B. Sukhija, L. Treven, C. Sancaktar, S. Blaes, S. Coros, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2023 [bibtex] [abstract] [pdf]
Efficient Exploration in Continuous-time Model-based Reinforcement Learning L. Treven, J. Hübotter, B. Sukhija, F. Dörfler, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2023 [bibtex] [abstract] [pdf]
2021
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models L. Treven, P. Wenk, F. Dörfler, A. Krause In Proc. Neural Information Processing Systems (NeurIPS), 2021 [bibtex] [abstract] [pdf]
Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory L. Treven, S. Curi, M. Mutný, A. Krause In Proc. Learning for Dynamics and Control (L4DC), 2021 [bibtex] [abstract] [pdf]