Published in AISTATS, 2025
Recommended citation: Kacprzyk, K., & van der Schaar, M. (2025). Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression. International Conference on Artificial Intelligence and Statistics. PMLR.
Published in ICLR, 2025
Recommended citation: Kacprzyk, K., & van der Schaar, M. (2025). No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs. International Conference on Learning Representations.
Published in NeurIPS, 2024
Recommended citation: Rauba, P., Seedat, N., Kacprzyk, K., & van der Schaar, M. Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. The Thirty-eighth Annual Conference on Neural Information Processing Systems.
Published in ICLR, 2024
Recommended citation: Kacprzyk, K., Liu, T., & van der Schaar, M. (2024) Towards Transparent Time Series Forecasting. International Conference on Learning Representations.
Published in AISTATS, 2024
Recommended citation: Kacprzyk, K., & van der Schaar, M. (2024). Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. International Conference on Artificial Intelligence and Statistics. PMLR.
Published in ICLR, 2024
Recommended citation: Kacprzyk*, K., Holt*, S., Berrevoets*, J., Qian, Z., & van der Schaar, M. (2024) ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. International Conference on Learning Representations.
Published in NeurIPS, 2023
Recommended citation: Kacprzyk, K., Qian, Z., & van der Schaar, M. (2023). D-CIPHER: Discovery of Closed-Form Partial Differential Equations. The Thirty-seventh Conference on Neural Information Processing Systems.
Published in ICLR, 2022
Recommended citation: Qian, Z., Kacprzyk, K., & van der Schaar, M. (2022) D-CODE: Discovering Closed-form ODEs from Observed Trajectories. International Conference on Learning Representations.
Published in Foundations and Trends® in Signal Processing, 2024
Recommended citation: Berrevoets, J., Kacprzyk, K., Qian, Z., & van der Schaar, M. (2024). Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality. Foundations and Trends® in Signal Processing, 18(3), 200-309.