Kavya Ravichandran
Preprints
Conference Publications
Where authorship is alphabetical, it follows the norm in theoretical computer science.
- A. Blum, E. Diana, K. Ravichandran , A. Tolbert. "Pessimism Traps and Algorithmic Interventions." To appear in FORC 2025.
- A. Blum, K. Ravichandran. "Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem." To appear in ALT 2025.
- A. Blum, K. Ravichandran. "A Model for Combinatorial Dictionary Learning and Inference." To appear in ALT 2025.
- ** C. Gerbelot, A. Karagulyan, S. Karp, K. Ravichandran , M. Stern, N. Srebro "Applying statistical learning theory to deep learning." Journal of Statistical Mechanics: Theory and Experiment, Volume 2024, October 2024 (Summer school on Statistical Physics & Machine learning, Les Houches 2022)
- M. Aliakbarpour, A.S. Biswas, K. Ravichandran , R. Rubinfeld. "Testing Tail Weight of a Distribution Via Hazard Rate." Proceedings of The 34th International Conference on Algorithmic Learning Theory in Proceedings of Machine Learning Research 201:34-81.
- M.S. Nacson, K. Ravichandran, D. Soudry, N. Srebro. "Implicit Bias of the Step Size in Linear Diagonal Neural Networks." Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research. 162:16270-16295.
- K. Ravichandran*, N. Braman*, A. Janowczyk, A. Madabhushi. "A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI." SPIE Medical Imaging: Houston, TX. February 2018.
* joint first authors.
** alphabetical with senior author last.
Other Work
- K. Ravichandran, Y. Dandi, S. Karp, F. Mignacco. "Learning from setbacks: the impact of adversarial initialization on generalization performance" Mathematics for Modern Machine Learning Workshop at NeurIPS 2023: New Orleans, LA. Paper and poster presentation: December 2023.
- S. De Silva*, R. Pinnamaneni*, K. Ravichandran*, A. Fadaq*, Y. Mei*, V. Sin*. "Yellow Fever in Brazil: Using Novel Data Sources to Produce Localized Policy Recommendations." Leveraging Big Data in Global Health. Edited by: Leo Anthony Celi, Maimuna S. Majumder, Patricia Ordoñez, Juan Sebastian Osorio, Kenneth E. Paik, Melek Somai. Springer. 2020.
- K. Ravichandran, A. Jain, A. Rakhlin. "Using Effective Dimension to Analyze Feature Transformations in Deep Neural Networks." Identifying and Understanding Phenomena in Deep Learning Workshop: Long Beach, CA. Paper and poster presentation: June 2019
- K. Ravichandran, A. Bhiwandiwalla, H. Tang. “Using Multi-Task Learning to Improve Out-of-Distribution Generalization in 2D Pose Estimation.” Women in Machine Learning: Montreal, Canada. Poster presentation: December 2018.
- N. Braman, K. Ravichandran, A. Janowczyk, J. Abraham, A. Madabhushi. “Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.” Journal of Clinical Oncology 2018 36:15_suppl, 582-582.
- K. Ravichandran, N. Braman, A. Janowczyk, A. Madabhushi. “A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from multiple contrast phases of baseline breast DCE-MRI.” Women in Machine Learning: Long Beach, CA. Poster presentation: December 2017.
- C. L. Pawlowski, W. Li, M. Sun, K. Ravichandran, D. Hickman, C. Kos, G. Kaur, A. Sen Gupta. "Platelet microparticle-inspired clot-responsive nanomedicine for targeted fibrinolysis." Biomaterials: 128 (2017) 94-108
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