PQ and TDS learning are equivalent in the distribution-free setting for Boolean classes, implying hardness for TDS halfspace learning but efficient algorithms with membership queries.
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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.
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Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift
PQ and TDS learning are equivalent in the distribution-free setting for Boolean classes, implying hardness for TDS halfspace learning but efficient algorithms with membership queries.
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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.