  We present the Local Self-Balancing sampler (LSB), a local Markov Chain Monte
Carlo (MCMC) method for sampling in purely discrete domains, which is able to
autonomously adapt to the target distribution and to reduce the number of
target evaluations required to converge. LSB is based on (i) a parametrization
of locally balanced proposals, (ii) a newly proposed objective function based
on mutual information and (iii) a self-balancing learning procedure, which
minimises the proposed objective to update the proposal parameters. Experiments
on energy-based models and Markov networks show that LSB converges using a
smaller number of queries to the oracle distribution compared to recent local
MCMC samplers.
