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Parallelizing Contextual Linear Bandits


May 21, 2021
Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter Bartlett, Michael I. Jordan


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Optimal Mean Estimation without a Variance


Dec 08, 2020
Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan

* Fixed typographical errors in Theorem 1.2, Lemmas 4.3 and C.8 

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Optimal Robust Linear Regression in Nearly Linear Time


Jul 16, 2020
Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett


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On the Theory of Transfer Learning: The Importance of Task Diversity


Jun 20, 2020
Nilesh Tripuraneni, Michael I. Jordan, Chi Jin


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Provable Meta-Learning of Linear Representations


Feb 26, 2020
Nilesh Tripuraneni, Chi Jin, Michael I. Jordan


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Debiasing Linear Prediction


Aug 06, 2019
Nilesh Tripuraneni, Lester Mackey


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Rao-Blackwellized Stochastic Gradients for Discrete Distributions


Oct 10, 2018
Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe

* 7 pages, 6 figures; submitted to AISTATS 2019 

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Averaging Stochastic Gradient Descent on Riemannian Manifolds


Jun 08, 2018
Nilesh Tripuraneni, Nicolas Flammarion, Francis Bach, Michael I. Jordan

* COLT 2018 

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Stochastic Cubic Regularization for Fast Nonconvex Optimization


Dec 05, 2017
Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan

* The first two authors contributed equally 

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Magnetic Hamiltonian Monte Carlo


Aug 19, 2017
Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard Turner

* 34th International Conference on Machine Learning (ICML 2017) 

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Lost Relatives of the Gumbel Trick


Jun 13, 2017
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller

* 34th International Conference on Machine Learning (ICML 2017) 

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A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models


Jun 09, 2015
Nilesh Tripuraneni, Shane Gu, Hong Ge, Zoubin Ghahramani


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