I'm a 5th-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.
Email: nilesh_tripuraneni AT berkeley DOT edu
Links: Google Scholar.
- Optimal Mean Estimation without a Variance. arXiv preprint. [arXiv]
- Optimal Robust Linear Regression in Nearly Linear Time. arXiv preprint. [arXiv]
- On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS 2020. [arXiv]
- Provable Meta-Learning of Linear Representations. arXiv preprint. [arXiv]
- Algorithms for Heavy-Tailed Statistics: Regression, Covariance Estimation, and Beyond. STOC 2020. [arXiv]
- Single Point Transductive Prediction. ICML 2020. [arXiv]
- Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019. [arXiv]
- Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT 2018. [arXiv]
- Stochastic Cubic Regularization for Fast Nonconvex Optimization. *Equal contribution. NIPS 2018 (Oral) [arXiv]
- Magnetic Hamiltonian Monte Carlo. ICML 2017. [arXiv]
- Lost Relatives of the Gumbel Trick. ICML 2017 (best paper honorable mention award). [arXiv]
- Quantitative criticism of literary relationships.
- Particle Gibbs for Infinite Hidden Markov Models. *Equal contribution. NIPS 2015 (poster). [NIPS]
- Bulk viscosity and cavitation in boost-invariant hydrodynamic expansion. JHEP 2010. [JHEP]