Author

Jakob Zeitler

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#machine learning Preprint Oct 2026

ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach

INLA-SPDE Spatio-Temporal Bayesian Optimization is introduced, the first scalable BO framework for spatio-temporal data, that models the log-intensity with a Log-Gaussian Cox Process and performs inference via Integrated Nested Laplace Approximation and Stochastic Partial Differential Equations (INLA-SPDE) approach.

Kai-Chuang Yang, H. Rue, Jakob Zeitler · 0 citations

#machine learning Preprint Oct 2026

ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding

Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy variables to identify effects under hidden confounding. However, nonparametric proximal estimation can be challenging in practice: recovering...

Christoph Müller, Ayub Kharel, Alex Luedtke et al. · 0 citations

Preprint Aug 2026

Partial Identification Learning with Categorical Treatments for Individualized Treatment Rules

We develop a partial identification learning framework for individualized treatment rules (ITRs) with categorical treatments, outcomes, and instrumental variables. Rather than relying on strong causal assumptions required for point identification, our framework leverages causal bounds to characterize the optimal treatm...

Johannes Hruza, Paweł Morzywołek, Jakob Zeitler et al. · 0 citations