Nilesh Tripuraneni

I'm a final-year Ph.D. student at U.C. Berkeley working with Michael Jordan. I'm broadly interested in machine learning, statistics and applications of machine learning in domains such as chemistry and biology. Before coming to Berkeley, I received an M.Phil in Information Engineering from the University of Cambridge where I was advised by Zoubin Ghahramani and a B.A. in Physics from Harvard University. During my Ph.D I have been fortunate to intern at Microsoft Research New England, Google Brain, and Amazon SCOT.

Email: nilesh_tripuraneni AT berkeley DOT edu

Links: Google Scholar.

Publications

• Covariate Shift in High-Dimensional Random Feature Regression. Nilesh Tripuraneni*, Ben Adlam*, Jeffrey Pennington* *Equal contribution. In Submission. [arXiv]
• Overparameterization Improves Robustness to Covariate Shift in High Dimensions. Nilesh Tripuraneni*, Ben Adlam*, Jeffrey Pennington* *Equal contribution. NeurIPS 2021. [arXiv]
• Parallelizing Contextual Linear Bandits. Jeffrey Chan*, Aldo Pacchiano*, Nilesh Tripuraneni*, Yun S. Song, Peter L. Bartlett, Michael I. Jordan. *Equal contribution. arXiv preprint. [arXiv]
• Optimal Mean Estimation without a Variance. Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan. arXiv preprint. [arXiv]
• Optimal Robust Linear Regression in Nearly Linear Time. Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett. arXiv preprint. [arXiv]
• On the Theory of Transfer Learning: The Importance of Task Diversity. Nilesh Tripuraneni, Michael I. Jordan, Chi Jin. NeurIPS 2020. [arXiv]
• Provable Meta-Learning of Linear Representations. Nilesh Tripuraneni, Chi Jin, Michael I. Jordan. ICML 2021. [arXiv]
• Algorithms for Heavy-Tailed Statistics: Regression, Covariance Estimation, and Beyond. Yeshwanth Cherapanamjeri, Samuel B. Hopkins, Tarun Kathuria, Prasad Raghavendra, Nilesh Tripuraneni. ($\alpha-\beta$ ordering) STOC 2020. [arXiv]
• Single Point Transductive Prediction. Nilesh Tripuraneni, Lester Mackey. ICML 2020. [arXiv]
• Rao-Blackwellized Stochastic Gradients for Discrete Distributions. Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe. ICML 2019. [arXiv]
• Averaging Stochastic Gradient Descent on Riemannian Manifolds. Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach, Michael I. Jordan. COLT 2018. [arXiv]
• Stochastic Cubic Regularization for Fast Nonconvex Optimization. Nilesh Tripuraneni*, Mitchell Stern*, Chi Jin, Jeffrey Regier, Michael I. Jordan. *Equal contribution. NIPS 2018 (Oral) [arXiv]
• Magnetic Hamiltonian Monte Carlo. Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner. ICML 2017. [arXiv]
• Lost Relatives of the Gumbel Trick. Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. ICML 2017 (best paper honorable mention award). [arXiv]
• Quantitative criticism of literary relationships. Joseph P. Dexter, Theodore Katz, Nilesh Tripuraneni, Tathagata Dasgupta, Ajay Kannan, James A. Brofos, Jorge A. Bonilla Lopez, Lea A. Schroeder, Adriana Casarez, Maxim Rabinovich, Ayelet Haimson Lushkov and Pramit Chaudhuri. PNAS. [PNAS]
• Particle Gibbs for Infinite Hidden Markov Models. Nilesh Tripuraneni*, Shixiang Gu*, Hong Ge, Zoubin Ghahramani. *Equal contribution. NIPS 2015 (poster). [NIPS]
• Bulk viscosity and cavitation in boost-invariant hydrodynamic expansion. Krishna Rajagopal, Nilesh Tripuraneni. JHEP 2010. [JHEP]