5 papers
Markov Chains Approximate Message Passing
Amit Rajaraman, David X. Wu
Markov chain Monte Carlo algorithms have long been observed to obtain near-optimal performance in various Bayesian inference settings. However, developing a supporting theory that…
Weak Poincaré Inequalities, Simulated Annealing, and Sampling from Spherical Spin Glasses
Brice Huang, Sidhanth Mohanty, Amit Rajaraman +1
There has been a recent surge of powerful tools to show rapid mixing of Markov chains, via functional inequalities such as Poincaré inequalities. In many situations, Markov chains…
Locally Stationary Distributions: A Framework for Analyzing Slow-Mixing Markov Chains
Kuikui Liu, Sidhanth Mohanty, Prasad Raghavendra +2
Many natural Markov chains fail to mix to their stationary distribution in polynomially many steps. Often, this slow mixing is inevitable since it is computationally intractable to…
Fast Mixing in Sparse Random Ising Models
Kuikui Liu, Sidhanth Mohanty, Amit Rajaraman +1
Motivated by the community detection problem in Bayesian inference, as well as the recent explosion of interest in spin glasses from statistical physics, we study the classical Gla…
Robust recovery for stochastic block models, simplified and generalized
Sidhanth Mohanty, Prasad Raghavendra, David X. Wu
We study the problem of : efficiently recovering communities in sparse stochastic block models in the presence of adversarial corruptions. In th…