1 citations · 2 across the 8 of their papers we have counts for
8 papers
Amortized Interventional Forecasting for Multivariate CIR Processes
Andreas Sauter, Sumit Sourabh, Drona Kandhai +1
Mean-reverting dynamics are pervasive in finance, and the Cox--Ingersoll--Ross (CIR) process is a standard model for the time series they produce, from short rates to credit defaul…
EvoIdeator: Evolving Scientific Ideas through Checklist-Grounded Reinforcement Learning
Andreas Sauter, Yuyue Zhao, Jacopo Urbani +5
Scientific idea generation is a cornerstone of autonomous knowledge discovery, yet the iterative evolution required to transform initial concepts into high-quality research proposa…
ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders
Andreas Sauter, Saber Salehkaleybar, Frank van Harmelen +2
Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, rest…
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction Experiments
Hüseyin Aydın, Kevin Godin-Dubois, Libio Goncalvez Braz +6
Reinforcement learning (RL) offers a general approach for modeling and training AI agents, including human-AI interaction scenarios. In this paper, we propose SHARPIE (Shared Human…
CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research
Andreas W M Sauter, Erman Acar, Aske Plaat
Research on causal effects often relies on synthetic data due to the scarcity of real-world datasets with ground-truth effects. Since current data-generating tools do not always me…
CAGE: Causality-Aware Shapley Value for Global Explanations
Nils Ole Breuer, Andreas Sauter, Majid Mohammadi +1
As Artificial Intelligence (AI) is having more influence on our everyday lives, it becomes important that AI-based decisions are transparent and explainable. As a consequence, the…
CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
Andreas W. M. Sauter, Nicolò Botteghi, Erman Acar +1
Causal discovery is the challenging task of inferring causal structure from data. Motivated by Pearl's Causal Hierarchy (PCH), which tells us that passive observations alone are no…
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
Thomas M. Moerland, Matthias Müller-Brockhausen, Zhao Yang +7
Due to the empirical success of reinforcement learning, an increasing number of students study the subject. However, from our practical teaching experience, we see students enterin…