Author

Vijaya Krishna Yalavarthi

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#machine learning Apr 2026

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting

CircuITS, a novel architecture for probabilistic IMTS forecasting based on sum-product networks (SPNs), is proposed, which is flexible in capturing intricate dependencies between time series channels while structurally guaranteeing valid joint distributions.

Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme · arXiv.org · 1 citation

#artificial intelligence Preprint Sep 2026

MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off

A model-based measure is introduced that compares a channel-dependent (CD) model to its channel-independent (CI) variant and only lagged MI and the CD gain recover every planted coupling, a model-based measure that compares a channel-dependent model to its channel-independent variant.

Ibram Abdelmalak, Mischa Putzke, Jungmin Choi et al. · 0 citations

Preprint Aug 2026

Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting

Two-stage Odd Residual Flows (TORF), a framework that decouples mean forecasting from uncertainty estimation, achieves state-of-the-art deterministic accuracy (NMAE) while providing strong density estimation performance (CRPS) on short and long-horizon forecasting.

Kiran Madhusudhanan, Christian Klötergens, Lars Schmidt-Thieme et al. · 0 citations

Preprint Aug 2026

Do Tabular Foundation Models Agree with Themselves?

This work proposes asking a different question: could a model's predictions result from any joint distribution?

Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme et al. · 3 citations