Research interests and publications
Feffer, M., Hirzel, M., Hoffman, S. C., Kate, K., Ram, P., & Shinnar, A. (2023). Searching for Fairer Machine Learning Ensembles. International Conference on Automated Machine Learning. PMLR, arXiv-v2, arXiv-v1
Lazuka, G., Anghel, A. S., Ram, P., Pozidis, H., Parnell, T. (2023). xCloudServing: Automated and Optimized ML Serving across Clouds. IEEE International Conference on Cloud Computing. paper
Dube, P., Salonidis, T., Ram, P., & Verma, A. (2023). Runtime Prediction of Machine Learning Algorithms in AutoML Systems. To appear in IEEE International Conference on Acoustics, Speech and Signal Processing. paper
Ram, P., Gray, A. G., Samulowitz, H. C., & Bramble, G. (2023). Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection. SIAM Internation Conference on Data Mining. arXiv, paper
Zhou, Y., Ram, P., Salonidis, T., Baracaldo, N., Samulowitz, H., & Ludwig, H. (2023). Single-shot Hyper-parameter Optimization for Federated Learning: A General Algorithm & Analysis. International Conference in Learning Representations. arXiv OpenReview slides (notable-top-25%)
Ram, P. (2022). On the Optimality Gap of Warm-Started Hyperparameter Optimization. In International Conference on Automated Machine Learning (pp. 12-1). PMLR. paper slides proofs
Hirzel, M., Kate, K., Ram, P., Shinnar, A., & Tsay, J. (2022). Gradual AutoML using Lale. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4794-4795). paper tutorial
Kishimoto, A., Bouneffouf, D., Marinescu, R., Ram, P., Rawat, A., Wistuba, M., & Botea, A. (2022). Bandit limited discrepancy search and application to machine learning pipeline optimization. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 9, pp. 10228-10237). paper
Kannan, A., Choudhury, A. R., Saxena, V., Raje, S., Ram, P., Verma, A., & Sabharwal, Y. (2021). HyperASPO: Fusion of Model and Hyper Parameter Optimization for Multi-objective Machine Learning. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 790-800). IEEE. website
Baudart, G., Hirzel, M., Kate, K., Ram, P., Shinnar, A., & Tsay, J. (2021). Pipeline combinators for Gradual AutoML. Advances in Neural Information Processing Systems, 34, 19705-19718. paper Lale
Subramanian, D., Wasserkrug, S., Murali, P., Phan, D., Ram, P., Davidovich, O., Ceugniet, X. & Katai, F. (2021). Data and Knowledge Driven Optimization Model Generation for Flow Based Optimization Problems. In INFORMS Annual Meeting. website
Marinescu, R., Kishimoto, A., Ram, P., Rawat, A., Wistuba, M., Palmes, P. P., & Botea, A. (2021). Searching for machine learning pipelines using a context-free grammar. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 10, pp. 8902-8911). paper
Katz, M., Ram, P., Sohrabi, S., & Udrea, O. (2020, June). Exploring context-free languages via planning: The case for automating machine learning. In Proceedings of the International Conference on Automated Planning and Scheduling (Vol. 30, pp. 403-411). paper
Liu, S., Ram, P., Vijaykeerthy, D., Bouneffouf, D., Bramble, G., Samulowitz, H., Wang, D., Conn, A. & Gray, A. (2020). An ADMM based framework for AutoML pipeline configuration. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 04, pp. 4892-4899). paper arXiv (IBM Research Pat Goldberg 2020 Finalist)
Wang, D., Ram, P., Weidele, D. K. I., Liu, S., Muller, M., Weisz, J. D., … & Amini, L. (2020). AutoAI: Automating the end-to-end AI lifecycle with humans-in-the-loop. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion (pp. 77-78). website paper
Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., … & Gray, A. (2019). Human-AI collaboration in data science: Exploring data scientists’ perceptions of automated AI. Proceedings of the ACM on human-computer interaction, 3(CSCW), 1-24. website paper
Hirzel, M., Ram, P. (2023). Oversampling to Repair Bias and Imbalance Simultaneously. AutoML 2023 Workshop. OpenReview
Davidovich, O., Ram, P., Wasserkrug, S., Subramaniam, S., Zhou, N., Phan, D., Murali P. & Nguyen, L. (2022). Addressing Solution Quality in Data Generated Optimization Models. AAAI-22 Workshop on AI for Decision Optimization (AI4DO@AAAI’22). paper
Marinescu, R., Pedapati, T., Vu, L., Palmes, P., Mummert, T., Kirchner, P., Subramaniam, S., Ram, P. & Bouneffouf, D. (2022). Automated Decision Optimization with Reinforcement Learning. AAAI-22 Workshop on AI for Decision Optimization (AI4DO@AAAI’22). paper
Zhou, Y., Ram, P., Salonidis, T., Baracaldo, N., Samulowitz, H., & Ludwig, H. (2021). FLoRA: Single-shot hyper-parameter optimization for federated learning. 1st NeurIPS Workshop on New Frontiers in Federated Learning (NFFL 2021), arXiv
Wasserkrug, S., Davidovich, O., Shindin, E., Subramanian, D., Ram, P., Mural, P., Phan, D., Zhou, N., & Nguyen, L. M. (2021). Ensuring the quality of optimization solutions in data generated optimization models. In IJCAI Decision Science for Optimization Workshop. paper
Hirzel, M., Kate, K. & Ram, P. (2021). Engineering fair machine learning pipelines. ICLR-21 Workshop on Responsible AI (RAI@ICLR) paper
Ram, P., Gray, A. G., & Samulowitz, H. (2021). Leveraging Theoretical Tradeoffs in Hyperparameter Selection for Improved Empirical Performance. In 8th ICML Workshop on Automated Machine Learning (AutoML). OpenReview
Baudart, G., Hirzel, M., Kate, K., Ram, P., & Shinnar, A. (2020). Lale: Consistent automated machine learning. KDD Workshop on Automation in Machine Learning (AutoML@KDD) arXiv
Ram, P., Liu, S., Vijaykeerthi, D., Wang, D., Bouneffouf, D., Bramble, G., Samulowitz, H., & Gray, A. G. (2020). Solving constrained CASH problems with ADMM. In 7th ICML Workshop on Automated Machine Learning (AutoML). paper arXiv