Natesh S. Pillai

Publication Activity (10 Years)

Years Active: 2007-2023
Publications (10 Years): 10

Top Topics

Johnson Lindenstrauss

Dirichlet Prior

Top Venues

J. Appl. Probab.

Publications

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent. NeurIPS (2023)
Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai
Weak Separation in Mixture Models and Implications for Principal Stratification. AISTATS (2022)
Andrew D. Davis, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai
Rate-optimal refinement strategies for local approximation MCMC. Stat. Comput. 32 (4) (2022)
Oren Mangoubi, Natesh S. Pillai, Aaron Smith
Simple conditions for metastability of continuous Markov chains. J. Appl. Probab. 58 (1) (2021)
Vishesh Jain, Natesh S. Pillai, Aaron Smith
Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform. CoRR (2020)
Oren Mangoubi, Natesh S. Pillai, Aaron Smith
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities? CoRR (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) (2018)
Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard
The use of a single pseudo-sample in approximate Bayesian computation. Stat. Comput. 27 (3) (2017)
James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson
Inefficiency of Data Augmentation for Large Sample Imbalanced Data. CoRR (2016)
Guillaume W. Basse, Aaron Smith, Natesh S. Pillai
Parallel Markov Chain Monte Carlo via Spectral Clustering. AISTATS (2016)
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 (2007)