Agarwal, Mayank

Investigating Explainability of Generative AI for Code through Scenario-based Design

Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., & Weisz, J. D. (2022) · 27th International Conference on Intelligent User Interfaces

Explainable Artificial Intelligence (XAI)Software Engineering Research

Cited by 219

IBM Federated Learning: an Enterprise Framework White Paper V0.1

Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 106

Better Together? An Evaluation of AI-Supported Code Translation

Weisz, J. D., Muller, M., Ross, S. I., Martinez, F., Houde, S., Agarwal, M., Talamadupula, K., & Richards, J. T. (2022) · 27th International Conference on Intelligent User Interfaces

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 69

Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks

Abdelaziz, I., Basu, K., Agarwal, M., Kumaravel, S., Stallone, M., Panda, R., Rizk, Y., Bhargav, G. P. S., Crouse, M., Gunasekara, C., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track

Topic ModelingNatural Language Processing Techniques

Cited by 22

Please be polite to your peers: a multi-task model for assessing the tone and objectivity of critiques of peer review comments

Bharti, P. K., Agarwal, M., & Ekbal, A. (2024) · Scientometrics

Sentiment Analysis and Opinion MiningHate Speech and Cyberbullying Detection

Cited by 6

Quality Estimation & Interpretability for Code Translation

Agarwal, M., Talamadupula, K., Houde, S., Martinez, F., Muller, M., Richards, J., Ross, S., & Weisz, J. D. (2020) · arXiv

Software Engineering ResearchNatural Language Processing Techniques

Cited by 3