This paper investigates limitations of learning tanh neural networks under finite-precision computations and Lp accuracy guarantees.

This paper establishes a connection between neural network approximation of score functions and approximation of probability distributions generated by reverse diffusion models.

This paper develops statistical learning theory for gradient boosting in Peaks-over-Threshold modeling using Generalized Pareto distributions, deriving error bounds and reducing gradient correlation.

The paper proposes FOAM, an adaptive damping method that stabilizes the Shampoo optimization algorithm by dynamically controlling damping and eigendecomposition frequency, thereby reducing staleness-i…

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 analyzes the finite-time behavior of nonlinear two-time-scale stochastic approximation and identifies a sharp boundary for decoupling the $k^{-1}$ rate.

This paper introduces approximation-preserving coresets, which provide weaker guarantees than strong coresets but stronger guarantees than weak coresets for preserving the costs of good solutions in b…

This paper proposes Error-conditioned Neural Solvers (ENS) for neural surrogate models to iteratively correct predictions by using the PDE residual field as input, achieving higher accuracy than optim…

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

This paper settles the complexity of three sketching problems in graphs and distributions.

The paper establishes tight upper and lower bounds on the statistical cost of approximate machine unlearning for smooth strongly convex losses, showing that the optimal unlearning rate depends critica…

This paper provides explicit error bounds for the infinite-width Gaussian-process limit of random neural networks using tensor programs and quantitative convergence theory in Wasserstein distance.

This paper presents conditions for achieving optimal uniformity in Quasi-Monte Carlo estimators using Sobol' sequences and Artin-Schreier polynomials.

This paper evaluates the effectiveness of Bayesian workflow for verifying statistical correctness of probabilistic programs written by language models, and compares it to unit tests and no feedback.

The paper improves Banaszczyk's inequality, providing a significantly better tail estimate for the discrete Gaussian measure on a lattice, which has applications in analyzing dual attacks against the…

This paper investigates the necessity of interaction for order-optimal 1-bit mean estimation in nonparametric finite-moment classes.

This paper proposes a novel estimator for the target linear coefficient in a partial linear model with black-box nuisance estimation and establishes its unimprovable error rate.