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Publication search results
found 12 matches
- 2023
- Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS 2023 - Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai:
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. CoRR abs/2305.14076 (2023) - 2022
- Andrew D. Davis, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai:
Rate-optimal refinement strategies for local approximation MCMC. Stat. Comput. 32(4): 60 (2022) - Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai:
Weak Separation in Mixture Models and Implications for Principal Stratification. AISTATS 2022: 5416-5458 - 2021
- Oren Mangoubi, Natesh S. Pillai, Aaron Smith:
Simple conditions for metastability of continuous Markov chains. J. Appl. Probab. 58(1): 83-105 (2021) - 2020
- Vishesh Jain, Natesh S. Pillai, Aaron Smith:
Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform. CoRR abs/2003.10069 (2020) - 2018
- Patrick R. Conrad, Andrew D. Davis, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith:
Parallel Local Approximation MCMC for Expensive Models. SIAM/ASA J. Uncertain. Quantification 6(1): 339-373 (2018) - Oren Mangoubi, Natesh S. Pillai, Aaron Smith:
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities? CoRR abs/1808.03230 (2018) - 2017
- Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard:
The use of a single pseudo-sample in approximate Bayesian computation. Stat. Comput. 27(3): 583-590 (2017) - 2016
- Guillaume W. Basse, Aaron Smith, Natesh S. Pillai:
Parallel Markov Chain Monte Carlo via Spectral Clustering. AISTATS 2016: 1318-1327 - James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson:
Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR abs/1605.05798 (2016) - 2007
- Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert:
Characterizing the Function Space for Bayesian Kernel Models. J. Mach. Learn. Res. 8: 1769-1797 (2007)
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