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cs.DS2025
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…
cs.DS2024
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…
cs.DS2024
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…