Publications (Google Scholar Profile)

Explainable Models via Compression of Tree Ensembles
S. Yan, S. Natarajan, S. Joshi, R. Khardon, P. Tadepalli. IJCLR 2022 (Journal track), under review.

A Statistical Relational Approach to Learning Distance-based GCNs
D. S. Dhami, S. Yan, S. Natarajan. Statistical Relational AI (StarAI) Workshop at IJCLR 2021.

Bridging Graph Neural Networks and Statistical Relational Learning: Relational One-Class GCN
D. S. Dhami, S. Yan, S. Natarajan. 2021.

Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem
D. S. Dhami, S. Yan, G. Kunapuli, D. Page, S. Natarajan. International Conference on Inductive Logic Programming (ILP) 2021.

Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
D. S. Dhami, S. Yan*, G. Kunapuli, D. Page, S. Natarajan. 19th International Conference in Artificial Intelligence in Medicine (AIME) 2021. (=equal contribution)

Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
D. S. Dhami, S. Yan, G. Kunapuli, D. Page, S. Natarajan. 2020.

The Curious Case of Stacking Boosted Relational Dependency Networks
S. Yan, D. S. Dhami, S. Natarajan. Proceedings on “I Can’t Believe It’s Not Better!” at NeurIPS Workshops, PMLR 2020.

Non-Parametric Learning of Gaifman Models
D. S. Dhami, S. Yan, G. Kunapuli, S. Natarajan. Statistical Relational AI (StarAI) Workshop at AAAI 2020.