REVIEW 6 cited by
Sampling from Spherical Spin Glasses in Total Variation via Algorithmic Stochastic Localization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Sampling from Spherical Spin Glasses in Total Variation via Algorithmic Stochastic Localization
read the original abstract
We consider the problem of algorithmically sampling from the Gibbs measure of a mixed $p$-spin spherical spin glass. We give a polynomial-time algorithm that samples from the Gibbs measure up to vanishing total variation error, for any model whose mixture satisfies $$\xi''(s) < \frac{1}{(1-s)^2}, \qquad \forall s\in [0,1).$$ This includes the pure $p$-spin glasses above a critical temperature that is within an absolute ($p$-independent) constant of the so-called shattering phase transition. Our algorithm follows the algorithmic stochastic localization approach introduced in (Alaoui, Montanari, Sellke, 20022). A key step of this approach is to estimate the mean of a sequence of tilted measures. We produce an improved estimator for this task by identifying a suitable correction to the TAP fixed point selected by approximate message passing (AMP). As a consequence, we improve the algorithm's guarantee over previous work, from normalized Wasserstein to total variation error. In particular, the new algorithm and analysis opens the way to perform inference about one-dimensional projections of the measure.
Forward citations
Cited by 6 Pith papers
-
Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model
Approximate stochastic localization plus conductance transfers yield a weak Poincaré inequality for the SK model at β < 1/2, enabling efficient Glauber sampling from a warm start.
-
On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy
Derives McDiarmid-type inequalities for dependent variables via approximate tensorization of entropy, with applications improving DKW rates to 1/sqrt(n) under weak dependence for log-concave measures.
-
Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2
A polynomial-time algorithm samples the SK model Gibbs measure with o(1) TVD error for β < 1/2 by combining potential Hessian ascent, stochastic localization, Jarzynski equality, and Glauber dynamics.
-
Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2
Polynomial-time algorithm samples the Sherrington-Kirkpatrick Gibbs measure at beta < 1/2 with o(1) TVD error by combining potential Hessian ascent, stochastic localization, covariance estimates, and Jarzynski equalit...
-
Potential Hessian Ascent: The Sherrington-Kirkpatrick Model
Presents the first iterative spectral algorithm for near-optimal solutions to random quadratic optimization over the hypercube, resolving Subag's conjecture via potential Hessian ascent and SDE approximation.
-
Lower bound on the mixing time of $p$-spin glasses
Proves an exponential lower bound on the mixing time of Glauber dynamics for the p-spin glass at inverse temperatures above C ln(p)/p for large p, via energy landscape analysis with Gaussian decompositions and a bottl...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.