Establishes an empirical concentration inequality for the empirical CDF of a functional on regenerative Markov chains, with data-dependent leading term and lower-order convergence bound.
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Random Walks and Intersection Local Time
12 Pith papers cite this work. Polarity classification is still indexing.
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Introduces downward conditional monotonicity for MMPP to obtain stochastic domination bounds that determine survival and extinction regimes for contact processes in finite-state random environments via QBD eigenvalue comparison.
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
DMW relaxes and lower-bounds GW by transporting distributions of sampled distance matrices, with finite-sample bounds depending on intrinsic dimension and sliced/multi-scale variants for computation.
Establishes a CLT for the competitive range of the k-th walk among N independent β-stable domain random walks in d/β ∈ [1,3/2), with an emerging competition term.
Extends Stein's method to symmetric matrix normal distributions with a Stein characterization, semigroup solution, and Wasserstein bound for Wishart approximation.
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
Proves convergence of critical multitype Bellman-Harris process with one infinite-mean lifetime to Poisson random measure concentrated on that type under space-lifetime condition ρ > γ/β.
Derives closed-form optimal counterfactually fair regressor via barycentric quantile map and proves Õ(n^{-1/3}) finite-sample fairness and risk bounds for discretized post-processing under mild assumptions.
Finite post-peak detector-frame windows for GW250114 yield a stable common-remnant Kerr interpretation after calibration on synthetic waveforms and robustness checks.
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
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Distance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
DMW relaxes and lower-bounds GW by transporting distributions of sampled distance matrices, with finite-sample bounds depending on intrinsic dimension and sliced/multi-scale variants for computation.