Rahul Kidambi I earned my PhD from University of Washington Seattle (Advisor: Sham M. Kakade). contact: rkidambi AT uw DOT edu | Google Scholar. |
ResearchI study topics in Machine Learning, Deep Learning and AI. Currently, my interests are in:
I consider issues in these topics with applications to Human-facing systems including recommendation systems and robotics. Research ThreadsStochastic Gradient Descent for large scale MLAlgorithmic Frameworks for Model-Based Interactive Learning |
PhD ThesisStochastic Gradient Descent For Modern Machine Learning: Theory, Algorithms And Applications,Rahul Kidambi. PhD Thesis, University of Washington Seattle, June 2019. [Link] PublicationsAsterisk [*] indicates alphabetical ordering of authors.Conference/Journal PapersAdam Block, Rahul Kidambi, Daniel Hill, Thorsten Joachims, Inderjit Dhillon. To Appear, ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022. ArXiv manuscript, abs/2204.10936, April 2022. Jonathan Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun. In Proc. Neural Information Processing Systems (NeurIPS), 2021. ArXiv manuscript, abs/2106.03207, May 2021. [Code] Rahul Kidambi, Jonathan Chang, Wen Sun. In Proc. Neural Information Processing Systems (NeurIPS), 2021. ArXiv manuscript, abs/2102.10769, February 2021. Rajat Sen, Alexander Rakhlin, Lexing Ying, Rahul Kidambi, Dean Foster, Daniel Hill, Inderjit Dhillon. In Proc. International Conference on Machine Learning (ICML), 2021. ArXiv manuscript, abs/2102.07800, February 2021. Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger. In Proc. International Conference on Machine Learning (ICML), 2021. ArXiv manuscript, abs/2102.06020, February 2021. Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims. In Proc. Neural Information Processing Systems (NeurIPS), 2020. ArXiv manuscript, abs/2005.05951, May 2020. [Project Page] Naman Agarwal, Sham M. Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford. In Proc. Algorithmic Learning Theory (ALT), 2020. ArXiv manuscript, abs/1711.08426, November 2017. Rong Ge, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli. In Proc. Neural Information Processing Systems (NeurIPS), 2019. ArXiv manuscript, abs/1904.12838, April 2019. [Slides] Rong Ge, Prateek Jain, Sham M. Kakade, Rahul Kidambi, Dheeraj M. Nagaraj, Praneeth Netrapalli. In Proc. Conference on Learning Theory (COLT), 2019. [COLT Proceedings] Rahul Kidambi, Praneeth Netrapalli, Prateek Jain, Sham M. Kakade. In International Conference on Learning Representations (ICLR), 2018. (Oral Presentation: 23/1002 submissions ≈ 2% Acceptance Rate.) Also an invited paper at Information Theory and Applications (ITA) workshop, San Diego, 2018. ArXiv manuscript, abs/1803.05591, March 2018. [Open Review] [ITA version] [Code] Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford. In Proc. Conference on Learning Theory (COLT), 2018. ArXiv manuscript, abs/1704.08227, April 2017. [COLT proceedings] [Video (Sham at MSR)] Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford. In Journal of Machine Learning Research (JMLR), Vol. 18 (223), July 2018. ArXiv manuscript, abs/1610.03774, October 2016. Updated, April 2018. [JMLR link] Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes. In Proc. Neural Information Processing Systems (NeurIPS), December 2015. (Spotlight Presentation) ArXiv manuscript, abs/1511.02163, November 2015. [NeurIPS proceedings] Rahul Kidambi, Min-Chi Shih, Kenneth Rose. In Proc. International Symposium on Biomedical Imaging (ISBI), May 2012. [ISBI proceedings] Invited/Workshop PapersPrateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Venkata Krishna Pillutla, Aaron Sidford. Invited paper at FSTTCS 2017. ArXiv manuscript, abs/1710.09430, October 2017. Rahul Kidambi, Sreeram Kannan. Invited paper at Allerton Conference on Communication, Control, and Computing, 2015. [Allerton proceedings] Technical ReportsDhruv Mahajan, Vivek Gupta, S. Sathiya Keerthi, Sundararajan Sellamanickam, Shravan Narayanamurthy, Rahul Kidambi. ArXiv manuscript, abs/1711.05482, November 2017. Rahul Kidambi, Vinod Nair, Sundararajan Sellamanickam, S. Sathiya Keerthi. ArXiv manuscript, abs/1311.2137, November 2013. Vinod Nair, Rahul Kidambi, Sundararajan Sellamanickam, S. Sathiya Keerthi, Johannes Gehrke, Vijay Narayanan. ArXiv manuscript, abs/1311.2276, November 2013. The dblp maintains a listing of my papers. 1. Earlier Version Titled "Parallelizing Stochastic Approximation Through Mini-Batching and Tail Averaging."↩ 2. Earlier Version Titled "Accelerating Stochastic Gradient Descent."↩ 3. Earlier Version Titled "The Step Decay Schedule: A Near Optimal Geometrically Decaying Learning Rate Procedure."↩ 4. Earlier Version Titled "Optimism is all you need: Model-based Imitation Learning from Observation Alone."↩ |
Academic ServiceI am also a member of the JMLR editorial board. |
TeachingSome classes I have TA'ed for include: |