Evaluating Stream Filtering for Entity Profile Updates for TREC 2013. - John R. Frank; Steven J. Bauer; Max Kleiman-Weiner; Daniel A. Roberts; Nilesh Tripuraneni; Ce Zhang 001; Christopher Ré; Ellen M. Voorhees; Ian Soboroff (TREC, 2013)

Particle Gibbs for Infinite Hidden Markov Models. - Nilesh Tripuraneni; Shixiang Gu; Hong Ge; Zoubin Ghahramani (NIPS, 2015)

Lost Relatives of the Gumbel Trick. - Matej Balog; Nilesh Tripuraneni; Zoubin Ghahramani; Adrian Weller (ICML, 2017)

Magnetic Hamiltonian Monte Carlo. - Nilesh Tripuraneni; Mark Rowland 001; Zoubin Ghahramani; Richard E. Turner (ICML, 2017)

Quantitative criticism of literary relationships. - 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 (Proc. Natl. Acad. Sci. USA, 2017)

Averaging Stochastic Gradient Descent on Riemannian Manifolds. - Nilesh Tripuraneni; Nicolas Flammarion; Francis R. Bach; Michael I. Jordan (COLT, 2018)

Stochastic Cubic Regularization for Fast Nonconvex Optimization. - Nilesh Tripuraneni; Mitchell Stern; Chi Jin 001; Jeffrey Regier; Michael I. Jordan (NeurIPS, 2018)

Rao-Blackwellized Stochastic Gradients for Discrete Distributions. - Runjing Liu; Jeffrey Regier; Nilesh Tripuraneni; Michael I. Jordan; Jon D. McAuliffe (ICML, 2019)

Debiasing Linear Prediction. - Nilesh Tripuraneni; Lester Mackey (CoRR, 2019)

Single Point Transductive Prediction. - Nilesh Tripuraneni; Lester Mackey (ICML, 2020)

On the Theory of Transfer Learning: The Importance of Task Diversity. - Nilesh Tripuraneni; Michael I. Jordan; Chi Jin 001 (NeurIPS, 2020)

Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond. - Yeshwanth Cherapanamjeri; Samuel B. Hopkins 001; Tarun Kathuria; Prasad Raghavendra; Nilesh Tripuraneni (STOC, 2020)

Optimal Robust Linear Regression in Nearly Linear Time. - Yeshwanth Cherapanamjeri; Efe Aras; Nilesh Tripuraneni; Michael I. Jordan; Nicolas Flammarion; Peter L. Bartlett (CoRR, 2020)

Provable Meta-Learning of Linear Representations. - Nilesh Tripuraneni; Chi Jin 001; Michael I. Jordan (ICML, 2021)

Overparameterization Improves Robustness to Covariate Shift in High Dimensions. - Nilesh Tripuraneni; Ben Adlam; Jeffrey Pennington (NeurIPS, 2021)

Parallelizing Contextual Linear Bandits. - Jeffrey Chan; Aldo Pacchiano; Nilesh Tripuraneni; Yun S. Song; Peter L. Bartlett; Michael I. Jordan (CoRR, 2021)

Covariate Shift in High-Dimensional Random Feature Regression. - Nilesh Tripuraneni; Ben Adlam; Jeffrey Pennington (CoRR, 2021)

Optimal Mean Estimation without a Variance. - Yeshwanth Cherapanamjeri; Nilesh Tripuraneni; Peter L. Bartlett; Michael I. Jordan (COLT, 2022)

Joint Representation Training in Sequential Tasks with Shared Structure. - Aldo Pacchiano; Ofir Nachum; Nilesh Tripuraneni; Peter L. Bartlett (CoRR, 2022)

Learning Beyond the Standard Model (of Data) - Nilesh Tripuraneni (2022)

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models. - Steve Yadlowsky; Lyric Doshi; Nilesh Tripuraneni (CoRR, 2023)

Choosing a Proxy Metric from Past Experiments. - Nilesh Tripuraneni; Lee Richardson; Alexander D'Amour; Jacopo Soriano; Steve Yadlowsky (KDD, 2024)

Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. - Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; et al. (CoRR, 2024)