1 citations · 3 across the 20 of their papers we have counts for
22 papers
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme +1
Probabilistic forecasting plays an essential role in risk-sensitive decision-making, particularly in long-horizon settings. However, existing approaches often face a fundamental tr…
Do Tabular Foundation Models Agree with Themselves?
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1
Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate the Bayesian posterior predic…
CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting
Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme
Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows. Existing c…
The Importance of Encoder Choice:A Tabular-Image Study
Ilia Koloiarov, Diego Coello de Portugal Mecke, Vijaya Krishna Yalavarthi +2
Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emph{plain MLP} as the encoder. Yet…
Valid and Expressive Copulas for Irregular Multivariate Time Series
Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1
We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of normalizing flows for univar…
Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Joint probabilistic modeling is essential for forecasting irregular multivariate time series (IMTS) to accurately quantify uncertainty. Existing approaches often struggle to balanc…