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Patrick Jahnke

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TMLR Journal 2025 Journal Article

Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification

Saleh GHOLAM ZADEH
Vaisakh Shaj
Patrick Jahnke
Gerhard Neumann
Tim Breitenbach

Minimizing loss functions is one important ingredient for machine learning to fit parameters such that the machine learning models extract relations hidden in the data. The smaller the loss function value on various splittings of a dataset, the better the machine learning model is assumed to perform. However, datasets are usually generated by dynamics consisting of deterministic components, where relations are clearly defined and consequently learnable, as well as stochastic parts where outcomes are random and thus not predictable. Depending on the amplitude of the deterministic and stochastic processes, the best achievable loss function value varies and is usually not known in real data science scenarios. In this research, a statistical framework is developed that provides measures to address the predictability of a target given the available input data and, after training a machine learning model, how much of the deterministic relations have been missed by the model. Consequently, the presented framework allows to differentiate model errors into unpredictable parts regarding the given input and a systematic miss of deterministic relations. The work extends the definition of model success or failure as well as the convergence of a training process. Moreover, it is demonstrated how such measures can enrich the procedure of model training. The framework is showcased with time series data on different synthetic and real-world datasets. The code is available at https://github.com/Saleh-Gholam-Zadeh/predictability_measure.

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TMLR Journal 2024 Journal Article

Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics

Andreas Boltres
Niklas Freymuth
Patrick Jahnke
Holger Karl
Gerhard Neumann

Finding efficient routes for data packets is an essential task in computer networking. The optimal routes depend greatly on the current network topology, state and traffic demand, and they can change within milliseconds. Reinforcement Learning can help to learn network representations that provide routing decisions for possibly novel situations. So far, this has commonly been done using fluid network models. We investigate their suitability for millisecond-scale adaptations with a range of traffic mixes and find that packet-level network models are necessary to capture true dynamics, in particular in the presence of TCP traffic. To this end, we present PackeRL, the first packet-level Reinforcement Learning environment for routing in generic network topologies. Our experiments confirm that learning-based strategies that have been trained in fluid environments do not generalize well to this more realistic, but more challenging setup. Hence, we also introduce two new algorithms for learning sub-second Routing Optimization. We present M-Slim, a dynamic shortest-path algorithm that excels at high traffic volumes but is computationally hard to scale to large network topologies, and FieldLines, a novel next-hop policy design that re-optimizes routing for any network topology within milliseconds without requiring any re-training. Both algorithms outperform current learning-based approaches as well as commonly used static baseline protocols, particularly in high-traffic volume scenarios. All findings are backed by extensive experiments in realistic network conditions in our fast and versatile training and evaluation framework.

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