Mahantesh M Halappanavar — accepted AI-conference papers

2026

Towards High Resolution Probabilistic Coastal Inundation Forecasting from Sparse Observations

Kazi Ashik Islam, Zakaria Mehrab, Mahantesh M Halappanavar, Henning Mortveit, Katragadda Sridhar, Jon Derek Loftis +2

AAAI 2026technical

Coastal flooding poses increasing threats to communities worldwide, necessitating accurate and hyper-local inundation forecasting for effective emergency response. However, real-world deployment of forecasting systems is often constrained by sparse sensor networks, where only a limited subset of loc

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2025

Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases

Yongjia Lei, Haoyu Han, Ryan A. Rossi, Franck Dernoncourt, Nedim Lipka, Mahantesh M Halappanavar +2

ACL 2025finding

Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods often retrieve these two types of knowledge in isolation without considering their mutual reinforcement and existing hybri…

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2023

Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains

Buddhika Nettasinghe, Samrat Chatterjee, Ramakrishna Tipireddy, Mahantesh M Halappanavar

ICML 2023poster

Conformal prediction is a widely used method to quantify the uncertainty of a classifier under the assumption of exchangeability (e.g., IID data). We generalize conformal prediction to the Hidden Markov Model (HMM) framework where the assumption of exchangeability is not valid. The key idea of the p…

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2023

Faster approximate subgraph counts with privacy

Dung Nguyen, Mahantesh M Halappanavar, Venkatesh Srinivasan, Anil Vullikanti

NeurIPS 2023poster

One of the most common problems studied in the context of differential privacy for graph data is counting the number of non-induced embeddings of a subgraph in a given graph. These counts have very high global sensitivity. Therefore, adding noise based on powerful alternative techniques, such as sm…

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