Natesh S. Pillai

According to our database, Natesh S. Pillai authored at least 11 papers between 2007 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent.

Tianle Liu, Promit Ghosal, Krishnakumar Balasubramanian, Natesh S. Pillai

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Rate-optimal refinement strategies for local approximation MCMC.

Andrew D. Davis, Youssef M. Marzouk, Aaron Smith, Natesh S. Pillai

Stat. Comput., 2022

Weak Separation in Mixture Models and Implications for Principal Stratification.

Nhat Ho, Avi Feller, Evan Greif, Luke Miratrix, Natesh S. Pillai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Simple conditions for metastability of continuous Markov chains.

Oren Mangoubi, Natesh S. Pillai, Aaron Smith

J. Appl. Probab., 2021

2020

Kac meets Johnson and Lindenstrauss: a memory-optimal, fast Johnson-Lindenstrauss transform.

Vishesh Jain, Natesh S. Pillai, Aaron Smith

CoRR, 2020

2018

Parallel Local Approximation MCMC for Expensive Models.

Patrick R. Conrad, Andrew D. Davis, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith

SIAM/ASA J. Uncertain. Quantification, 2018

Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities?

Oren Mangoubi, Natesh S. Pillai, Aaron Smith

CoRR, 2018

2017

The use of a single pseudo-sample in approximate Bayesian computation.

Luke Bornn, Natesh S. Pillai, Aaron Smith, Dawn Woodard

Stat. Comput., 2017

2016

Inefficiency of Data Augmentation for Large Sample Imbalanced Data.

James E. Johndrow, Aaron Smith, Natesh S. Pillai, David B. Dunson

CoRR, 2016

Parallel Markov Chain Monte Carlo via Spectral Clustering.

Guillaume W. Basse, Aaron Smith, Natesh S. Pillai

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2007

Characterizing the Function Space for Bayesian Kernel Models.

Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert

J. Mach. Learn. Res., 2007