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University of Texas at Austin
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Polynomial-time sampling in spin glasses and sparse Bayesian regression is tractable deep into low-temperature regimes, slashing the measurement barrier for spike-and-slab posteriors from $k^3$ to $k^{3/2}$ while approaching the Almeida鈥揟houless phase boundary.
Weakening error assumptions reveals that unbiased sampling may be impossible, reshaping our understanding of tractable sampling methods.
Sparse recovery requires quadratically more samples in adaptive settings than oblivious ones, a surprising divergence from the well-understood $\ell_2$ norm.
