The paper shows that a random linear map behaves similarly to a fully random map in terms of expected loads and concentration of bin loads.

The paper proposes a novel two-stage framework to differentially privatize tables of counts by focusing on preserving the accuracy of the underlying count distribution, introducing the specialized cyc…

This paper derives bounds on the probability of incorrect clustering of noisy short sequences using statistically optimal rules, focusing on DNA storage decoders.

The paper proves that binary sequences derived from permutations are always X-rays of doubly stochastic matrices by relaxing constraints.

This paper proves conditions for efficiently approximating expectations of certain functions with respect to standard Gaussian or symmetric exponential probability measures.

Fine-tunes a 7B model to predict concurrent program next events by leveraging nondeterminism in scheduler signals

This paper studies the maximum number of jobs a queueing system can serve covertly without being detected.

This paper develops new formulations of Hamiltonian Monte Carlo (HMC) for infinite-dimensional parameter spaces in Haskell using lazy evaluation, providing analysis for automatic differentiation and M…

This paper proposes a Bayesian framework to enhance membership inference attacks against released statistics by incorporating prior knowledge about the population's attribute dependency structure, out…

This paper provides non-asymptotic and explicit estimates for the exponential deviation inequalities of the HyperLogLog estimator.

This paper derives the asymptotic distribution of the k-th maximum order statistics of signal to noise ratio for a κ-μ fading channel using extreme value theory, and applies these results to various c…

This paper proposes B-ExposNet, a Bayesian neural network for spatial exposure estimation with uncertainty quantification.

This paper provides a probabilistic derivation of multiparameter signless Stirling numbers and their q-analogues.

This paper provides an information-theoretic analysis of Yager's negation of probability distributions and its generalizations.

This paper provides an introduction to modern developments in combinatorial discrepancy theory, focusing on algorithmic and convex geometric ideas.

This paper introduces a Gaussian approximation for the finite-sample distribution of ridge regression estimator, allowing for heteroskedasticity and autocorrelation in the data generating process. Two…

The paper develops a stochastic framework using Laplace-Carson transforms to model and quantify optimal proactive defense timing against a single cyberattack, providing closed-form solutions for defen…