The paper introduces lazy Streett supermartingales and their lexicographic extension to certify almost-sure satisfaction of omega-regular properties with polynomial templates under a broad class of sa…

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

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

This paper characterizes how estimation error in covariance matrices affects the global minimum-variance portfolio and derives a decision geometry for regret.

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

This paper presents a self-balancing sampler for sequential sampling that achieves faster convergence to a desired target law while maintaining unpredictability.

Introduces Self-Similar Generative Estimation (SS-GEN), a method for simulating multivariate tail events and estimating rare-event probabilities using deep generative models based on asymptotic tail s…

The paper analyzes a new class of asynchronous adaptive first-order optimization methods and proves their stochastic convergence rate is O(1/sqrt{t}) for non-convex functions.

The paper introduces MINTS, a minimalist Bayesian framework that simplifies sequential decision-making by placing priors only on the optimum location, allowing for the incorporation of structural cons…

This paper studies the problem of aggregating calibrated Bayesian experts into a new calibrated expert.

This paper investigates the convergence and stability of equilibria in bimatrix two-player games using the optimistic exponential weights method, allowing step sizes to differ.

The paper proposes a new, optimal estimator for semiparametric inference that improves upon standard double machine learning (DML) rates by eliminating the first-order stochastic error of nuisance fun…

This paper derives the complete theory of covariance regret functional for decision making, providing insights on steepest-descent directions and boundary-optimal solutions.

This paper shows that the pricing of outcomes in prediction markets is significantly influenced by the financial friction of delayed settlement, quantifying this effect using an annualized settlement…

This paper introduces a new tamed stochastic gradient Hamiltonian Monte Carlo (tSGHMC) algorithm for sampling and optimization problems with large stochastic gradients, providing error bounds and perf…