activity
20242026
collaborators

7 papers

stat.ML2026

Operator Neural Jump ODEs: -optimal prediction in function spaces

Florian Krach, Oliver Löthgren, Josef Teichmann

In this paper, we study the extension of Neural Jump ODEs to infinite-dimensional function spaces. In particular, the underlying process now takes values in $L^2(Ξ, \mathbb{R}^…

stat.ML2025

Neural Jump ODEs as Generative Models

Robert A. Crowell, Florian Krach, Josef Teichmann

In this work, we explore how Neural Jump ODEs (NJODEs) can be used as generative models for Itô processes. Given (discrete observations of) samples of a fixed underlying Itô proces…

stat.AP2025

Revealing the temporal dynamics of antibiotic anomalies in the infant gut microbiome with neural jump ODEs

Anja Adamov, Markus Chardonnet, Florian Krach +3

Detecting anomalies in irregularly sampled multi-variate time-series is challenging, especially in data-scarce settings. Here we introduce an anomaly detection framework for irregu…

cs.CL2025

IMProofBench: Benchmarking AI on Research-Level Mathematical Proof Generation

Johannes Schmitt, Gergely Bérczi, Jasper Dekoninck +57

As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on research-level tasks at the frontier of…

math.PR2025

Universal approximation property of neural stochastic differential equations

Anna P. Kwossek, David J. Prömel, Josef Teichmann

We identify various classes of neural networks that are able to approximate continuous functions locally uniformly subject to fixed global linear growth constraints. For such neura…

stat.ML2024

Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

Florian Krach, Josef Teichmann

The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially in…

q-fin.CP2024

Robust Utility Optimization via a GAN Approach

Florian Krach, Josef Teichmann, Hanna Wutte

Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a…