Jurek Leonhardt, Henrik Müller, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand (2024), Efficient Neural Ranking Using Forward Indexes and Lightweight Encoders, In ACM Transactions on Information Systems Volume 42.

Ziyu Li, Hilco Van Der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai (2024), Model Selection with Model Zoo via Graph Learning, In Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024 p.1296-1309, IEEE.

Y. Xue, D. Kudenko, M. Khosla (2023), Graph learning-based generation of abstractions for reinforcement learning, In Neural Computing and Applications.

Iyiola E Olatunji, Mandeep Rathee, Thorben Funke, M. Khosla (2023), Private Graph Extraction via Feature Explanations, In Proceedings on Privacy Enhancing Technologies 2023(2) p.59-78.

Kerstin Beer, Megha Khosla, Julius Köhler, Tobias J. Osborne, Tianqi Zhao (2023), Quantum machine learning of graph-structured data, In Physical Review A Volume 108.

Thorben Funke, Megha Khosla, Mandeep Rathee, Avishek Anand (2023), Zorro: Valid, sparse, and stable explanations in graph neural networks, In IEEE Transactions on Knowledge & Data Engineering Volume 35 p.8687-8698.

Thi Ngan Dong, Johanna Schrader, Stefanie Mucke, Megha Khosla (2022), A message passing framework with multiple data integration for miRNA-disease association prediction, In Scientific Reports Volume 12.

Iyiola E. Olatunji, Jens Rauch, Matthias Katzensteiner, Megha Khosla (2022), A review of anonymization for healthcare data, In Big Data Volume 12 p.538-555.

Jurek Leonhardt, Koustav Rudra, Megha Khosla, Abhijit Anand, Avishek Anand (2022), Efficient Neural Ranking using Forward Indexes, In Proceedings of the ACM Web Conference 2022.

Thi Ngan Dong, Stefanie Mucke, Megha Khosla (2022), MuCoMiD: A Multitask graph Convolutional Learning Framework for miRNA-Disease Association Prediction, In IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 19 p.3081-3092.