Author

S. Natarajan

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Preprint Aug 2026

A Compositional Theory of Curvature in Probabilistic Circuits

Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood. Recent work regularizes this trace globally to bias learning toward flatter, better generalizi...

Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay et al. · 0 citations

Preprint Aug 2026

Tydra: An Efficient Hybrid Model for Tabular Data

Tydra is introduced, a hybrid Transformer-State Space Model (SSM) architecture for tabular in-context learning that interleaves attention and SSM layers that reduces inference time by 30% relative to TabPFN while retaining much of its predictive performance.

Mieszko Komisarczyk, Saurabh Mathur, Maurice Kraus et al. · 1 citation

#artificial intelligence Conference Feb 2024

Building Expressive and Tractable Probabilistic Generative Models: A Review

A unified perspective on the inherent trade-offs between expressivity and tractability is provided, highlighting the design principles and algorithmic extensions that have enabled building expressive and efficient PCs, and a taxonomy of the field is provided.

Sahil Sidheekh, S. Natarajan · International Joint Conferen... · 13 citations