The paper introduces GLIDE, an open-source Python library that unifies multiple state-of-the-art Prediction-Powered Inference (PPI) estimators and samplers to provide reliable, debiased estimates and…

This paper introduces gradient inference, a new approach to developing sound and efficient gradient estimators for probabilistic programs by reducing gradient estimation to a related probabilistic inf…

This paper argues that the choice of divergence in statistical inference can be justified by requiring invariance to problem reformulations, leading to the selection of the Kullback-Leibler divergence…

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 proposes statistical procedures to identify coordinates responsible for change-points in multivariate time series data.

The paper presents a framework for model selection and parameter estimation using large language models and neural simulation-based inference.

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

This paper analyzes the reliability of efficient membership inference attack (MIA) evaluation methods, demonstrating that standard aggregation techniques introduce biases that compromise accurate vuln…

The paper challenges the conclusion that LLMs lack reasoning by demonstrating that reported performance drops on GSM-Symbolic are often statistically weak and partially attributable to dataset biases,…

This paper evaluates the reliability of using item response theory (IRT) models for AI benchmarking, comparing four estimation tools under various simulation conditions.

The paper proposes a formal verification framework for statistical programs in Python using Why3-py and extends StatWhy to verify meta-analysis methods.

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

This paper evaluates the ability of large language models to recover and express evidence grades from clinical claims, finding that while they can recover the grades, they do not consistently express…