8 papers
On the Pseudo-Mixing of Kac's Walk
Natesh S. Pillai, Aaron Smith, Vinod Vaikuntanathan
Motivated by a conjecture of Vaikuntanathan and Zamir, we study the pseudo-mixing of Kac's walk on : whether short trajectories are indistinguishable from Haar meas…
Kac's walk on rotation matrices mixes in steps
Natesh S. Pillai, Aaron Smith
Kac's walk on the rotation group, introduced by Hastings in 1970, is an important high-dimensional Markov chain with applications in statistical physics, statistics, cryptography,…
Microergodicity implies orthogonality of Matérn fields on bounded domains in
Natesh S. Pillai
Matérn random fields are one of the most widely used classes of models in spatial statistics. The fixed-domain identifiability of covariance parameters for stationary Matérn Gaus…
A Heavily Right Strategy for Statistical Inference with Dependent Studies in Any Dimension
Tianle Liu, Xiao-Li Meng, Natesh S. Pillai
We leverage recent advances in heavy-tail approximations for global hypothesis testing with dependent studies to construct approximate confidence regions without modeling or estima…
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions
Natesh S. Pillai
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm that incorporates the gradient of the logarithm of the target density in its proposal distribution. In an…
Policy Gradients for Optimal Parallel Tempering MCMC
Daniel Zhao, Natesh S. Pillai
Parallel tempering is a meta-algorithm for Markov Chain Monte Carlo that uses multiple chains to sample from tempered versions of the target distribution, enhancing mixing in multi…