Karthik Somayaji NS — accepted AI-conference papers

2026

GAUSS: Graph-Assisted Uncertainty Quantification using Structure and Semantics for Long-Form Generation in LLMs

Karthik Somayaji NS, Yuxuan Yin, Peng Li

ICML 2026poster

In critical domains like clinical reporting, legal analysis, and policy drafting, large language models (LLMs) are increasingly expected to produce extended, fact‑rich narratives rather than isolated sentences. Reliable uncertainty quantification in such long‑form outputs is crucial. Existing techni…

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2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

Yu Wang, Yuxuan Yin, Karthik Somayaji NS, Ján Drgoňa, Malachi Schram, Mahantesh Halappanavar +2

AAAI 2024technical

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leve…

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