Statistical Learning
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Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
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We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence.
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Tester-Learners for Halfspaces: Universal Algorithms
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In the testable learning model, the learning algorithm, or tester-learner, is given access to labeled examples from an unknown distribution and may either reject or accept the unknown distribution. If it accepts, it must successfully produce a near-optimal hypothesis.
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Trade-offsand Guaranteesof Adversarial Representation Learningfor Information Obfuscation
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Assumetheconditionsin Lemma 3.4 hold, then8h 2 H, ACC0(h f)+ ACC1(h f) 2
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