Hi! I’m Sebastian Curi, I’m a Ph.D. student at ETH Zurich, under the supervision of Andreas Krause. Currently, I’m doing an internship at Google, under the supervision of Gabriel Dulac-Arnold and Marcin Andrychowicz. Previously, I did a stay at Gergely’s Neu Lab in Universtat Pompeu Fabra.
I’m interested in the intersection between theory and practice of Reinforcement Learning/Control Systems. In particular, I focus on how to bring these algorithms into practical methods. I also care about the robustness, safety, sample-efficiency, and risk-aversity (to name some aspects) of data-driven decision-making.
- Curi, S., Bogunovic, I., & Krause, A. (2021). Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning. International Conference on Machine Learning (ICML).
- Treven, L., Curi, S., Mutny, M., & Krause, A. (2021). Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory. Learning for Dynamics and Control (L4DC).
- Serrano, J. B., Curi, S., Krause, A., & Neu, G. (2021). Logistic Q-Learning. International Conference on Artificial Intelligence and Statistics, 3610–3618.
- Urpı́ Núria Armengol, Curi, S., & Krause, A. (2021). Risk-Averse Offline Reinforcement Learning. International Conference on Learning Representations. https://openreview.net/forum?id=TBIzh9b5eaz
- Curi, S., Berkenkamp, F., & Krause, A. (2020). Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning. Neural Information Processing Systems (NeurIPS), 33.
- Curi, S., Levy, K., Jegelka, S., Krause, A., & others. (2020). Adaptive sampling for stochastic risk-averse learning. Neural Information Processing Systems (NeurIPS), 33.
- Curi, S., Melchior, S., Berkenkamp, F., & Krause, A. (2020). Structured Variational Inference in Partially Observable Unstable Gaussian Process State Space Models. Learning for Dynamics and Control (L4DC), 147–157.
- Curi, S., Levy, K. Y., & Krause, A. (2019). Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations. 2019 IEEE 58th Conference on Decision and Control (CDC), 4115–4120.
- Borsos, Z., Curi, S., Levy, K. Y., & Krause, A. (2019). Online variance reduction with mixtures. International Conference on Machine Learning (ICML), 705–714.
- Fiducioso, M., Curi, S., Schumacher, B., Gwerder, M., & Krause, A. (2019). Safe contextual Bayesian optimization for sustainable room temperature PID control tuning. International Joint Conference on Artificial Intelligence, (IJCAI-19), 5850–5856.
- Curi, S., Groß, D., & Dörfler, F. (2017). Control of low-inertia power grids: A model reduction approach. 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5708–5713.
- Bright, L. Z., Handley, M., Chien, I., Curi, S., Brownworth, L. A., D’hers, S., Bernier, U. R., Gurman, P., & Elman, N. M. (2016). Analytical models integrated with satellite images for optimized pest management. Precision Agriculture, 17(5), 628–636.
- Curi, S., Mas, I., & Pena, R. S. (2014). Autonomous flight of a commercial quadrotor. IEEE Latin America Transactions, 12(5), 853–858.
- Chi, A., Curi, S., Clayton, K., Luciano, D., Klauber, K., Alexander-Katz, A., D’hers, S., & Elman, N. M. (2014). Rapid Reconstitution Packages (RRPs) implemented by integration of computational fluid dynamics (CFD) and 3D printed microfluidics. Drug Delivery and Translational Research, 4(4), 320–333.