Adversarially robust (1+ε)-approximate F_p estimation for all p∈[0,2] in polylog space is achieved via implicit isometric embeddings into Hilbert space and regularized kernel regression, plus a weak equivalence theorem between oblivious sketching and robust streaming.
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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.
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Adversarial Robustness for Small Frequency Moments and a Weak Equivalence Theorem for Turnstile Streams
Adversarially robust (1+ε)-approximate F_p estimation for all p∈[0,2] in polylog space is achieved via implicit isometric embeddings into Hilbert space and regularized kernel regression, plus a weak equivalence theorem between oblivious sketching and robust streaming.
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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.