Nilesh Tripuraneni

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 28

Top Topics

Weibull Distribution

Covariate Shift

Top Venues

Publications

Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
Choosing a Proxy Metric from Past Experiments. KDD (2024)
Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
Choosing a Proxy Metric from Past Experiments. CoRR (2023)
Steve Yadlowsky, Lyric Doshi, Nilesh Tripuraneni
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models. CoRR (2023)
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
Optimal Mean Estimation without a Variance. COLT (2022)
Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR (2022)
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS (2021)
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan
Parallelizing Contextual Linear Bandits. CoRR (2021)
Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
Covariate Shift in High-Dimensional Random Feature Regression. CoRR (2021)
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
Provable Meta-Learning of Linear Representations. ICML (2021)
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS (2020)
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett
Optimal Robust Linear Regression in Nearly Linear Time. CoRR (2020)
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan
Optimal Mean Estimation without a Variance. CoRR (2020)
Yeshwanth Cherapanamjeri, Samuel B. Hopkins, Tarun Kathuria, Prasad Raghavendra, Nilesh Tripuraneni
Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond. STOC (2020)
Nilesh Tripuraneni, Lester Mackey
Single Point Transductive Prediction. ICML (2020)
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin
On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR (2020)
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan
Provable Meta-Learning of Linear Representations. CoRR (2020)
Yeshwanth Cherapanamjeri, Samuel B. Hopkins, Tarun Kathuria, Prasad Raghavendra, Nilesh Tripuraneni
Algorithms for Heavy-Tailed Statistics: Regression, Covariance Estimation, and Beyond. CoRR (2019)
Nilesh Tripuraneni, Lester Mackey
Debiasing Linear Prediction. CoRR (2019)
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML (2019)
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR (2018)
Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR (2018)
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS (2018)
Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT (2018)
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
Lost Relatives of the Gumbel Trick. CoRR (2017)
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan
Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR (2017)
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner
Magnetic Hamiltonian Monte Carlo. ICML (2017)
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller
Lost Relatives of the Gumbel Trick. ICML (2017)
Joseph P. Dexter, Theodore Katz, Nilesh Tripuraneni, Tathagata Dasgupta, Ajay Kannan, James Brofos, Jorge A. Bonilla Lopez, Lea A. Schroeder, Adriana Casarez, Maxim Rabinovich, Ayelet Haimson Lushkov, Pramit Chaudhuri
Quantitative criticism of literary relationships. Proc. Natl. Acad. Sci. USA 114 (16) (2017)
Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani
Particle Gibbs for Infinite Hidden Markov Models. NIPS (2015)
John R. Frank, Steven J. Bauer, Max Kleiman-Weiner, Daniel A. Roberts, Nilesh Tripuraneni, Ce Zhang, Christopher Ré, Ellen M. Voorhees, Ian Soboroff
Evaluating Stream Filtering for Entity Profile Updates for TREC 2013. TREC (2013)