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ORCIDNilesh Tripuraneni
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2020 – today
- 2024
[c14]Nilesh Tripuraneni
, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky:
Choosing a Proxy Metric from Past Experiments. KDD 2024: 5803-5812- [i16]Kiran Vodrahalli, Santiago Ontanon, Nilesh Tripuraneni, Kelvin Xu, Sanil Jain, Rakesh Shivanna, Jeffrey Hui, Nishanth Dikkala, Mehran Kazemi, Bahare Fatemi, Rohan Anil, Ethan Dyer, Siamak Shakeri, Roopali Vij, Harsh Mehta, Vinay V. Ramasesh, Quoc Le, Ed H. Chi, Yifeng Lu, Orhan Firat, Angeliki Lazaridou, Jean-Baptiste Lespiau, Nithya Attaluri, Kate Olszewska:
Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries. CoRR abs/2409.12640 (2024) - 2023
- [i15]Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky:
Choosing a Proxy Metric from Past Experiments. CoRR abs/2309.07893 (2023) - [i14]Steve Yadlowsky, Lyric Doshi, Nilesh Tripuraneni:
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models. CoRR abs/2311.00871 (2023) - 2022
- [b1]Nilesh Tripuraneni:
Learning Beyond the Standard Model (of Data). University of California, Berkeley, USA, 2022 - [c13]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. COLT 2022: 356-357 - [i13]Aldo Pacchiano, Ofir Nachum, Nilesh Tripuraneni, Peter L. Bartlett:
Joint Representation Training in Sequential Tasks with Shared Structure. CoRR abs/2206.12441 (2022) - 2021
- [c12]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. ICML 2021: 10434-10443 - [c11]Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Overparameterization Improves Robustness to Covariate Shift in High Dimensions. NeurIPS 2021: 13883-13897 - [i12]Jeffrey Chan, Aldo Pacchiano, Nilesh Tripuraneni, Yun S. Song, Peter L. Bartlett, Michael I. Jordan:
Parallelizing Contextual Linear Bandits. CoRR abs/2105.10590 (2021) - [i11]Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington:
Covariate Shift in High-Dimensional Random Feature Regression. CoRR abs/2111.08234 (2021) - 2020
- [c10]Nilesh Tripuraneni, Lester Mackey:
Single Point Transductive Prediction. ICML 2020: 9593-9602 - [c9]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. NeurIPS 2020 - [c8]Yeshwanth Cherapanamjeri, Samuel B. Hopkins, Tarun Kathuria, Prasad Raghavendra, Nilesh Tripuraneni:
Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond. STOC 2020: 601-609 - [i10]Nilesh Tripuraneni, Chi Jin, Michael I. Jordan:
Provable Meta-Learning of Linear Representations. CoRR abs/2002.11684 (2020) - [i9]Nilesh Tripuraneni, Michael I. Jordan, Chi Jin:
On the Theory of Transfer Learning: The Importance of Task Diversity. CoRR abs/2006.11650 (2020) - [i8]Yeshwanth Cherapanamjeri, Efe Aras, Nilesh Tripuraneni, Michael I. Jordan, Nicolas Flammarion, Peter L. Bartlett:
Optimal Robust Linear Regression in Nearly Linear Time. CoRR abs/2007.08137 (2020) - [i7]Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. CoRR abs/2011.12433 (2020)
2010 – 2019
- 2019
- [c7]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. ICML 2019: 4023-4031 - [i6]Nilesh Tripuraneni, Lester Mackey:
Debiasing Linear Prediction. CoRR abs/1908.02341 (2019) - [i5]Yeshwanth Cherapanamjeri, Samuel B. Hopkins, Tarun Kathuria, Prasad Raghavendra, Nilesh Tripuraneni:
Algorithms for Heavy-Tailed Statistics: Regression, Covariance Estimation, and Beyond. CoRR abs/1912.11071 (2019) - 2018
- [c6]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. COLT 2018: 650-687 - [c5]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. NeurIPS 2018: 2904-2913 - [i4]Nilesh Tripuraneni, Nicolas Flammarion, Francis R. Bach, Michael I. Jordan:
Averaging Stochastic Gradient Descent on Riemannian Manifolds. CoRR abs/1802.09128 (2018) - [i3]Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. CoRR abs/1810.04777 (2018) - 2017
- [j1]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): E3195-E3204 (2017) - [c4]Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller:
Lost Relatives of the Gumbel Trick. ICML 2017: 371-379 - [c3]Nilesh Tripuraneni, Mark Rowland, Zoubin Ghahramani, Richard E. Turner:
Magnetic Hamiltonian Monte Carlo. ICML 2017: 3453-3461 - [i2]Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller:
Lost Relatives of the Gumbel Trick. CoRR abs/1706.04161 (2017) - [i1]Nilesh Tripuraneni, Mitchell Stern, Chi Jin, Jeffrey Regier, Michael I. Jordan:
Stochastic Cubic Regularization for Fast Nonconvex Optimization. CoRR abs/1711.02838 (2017) - 2015
- [c2]Nilesh Tripuraneni, Shixiang Gu, Hong Ge, Zoubin Ghahramani:
Particle Gibbs for Infinite Hidden Markov Models. NIPS 2015: 2395-2403 - 2013
- [c1]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
Coauthor Index
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