Finite-size general security for DPSK QKD is achieved with positive key rates for 10^5 signals beyond 12 dB loss via variable-length entropy accumulation and conic optimization.
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PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Weak convergence rates of Markov transition kernels imply variance convergence bounds for Lipschitz functions and chi-squared divergence bounds under reversibility with Lipschitz initial densities.
citing papers explorer
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Finite-size general security for differential phase shift keying via variable-length quantum key distribution
Finite-size general security for DPSK QKD is achieved with positive key rates for 10^5 signals beyond 12 dB loss via variable-length entropy accumulation and conic optimization.
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
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Implications of weak convergence rates of Markov transition kernels
Weak convergence rates of Markov transition kernels imply variance convergence bounds for Lipschitz functions and chi-squared divergence bounds under reversibility with Lipschitz initial densities.