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

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 measures the lower bound for the shortest program generating a sequence, proving a conservation law and providing a deterministic engine to recover generating programs for certain sequences…

This paper presents a compositional cost analysis for probabilistic programs with hierarchical cost structures, allowing computation of mean and higher moments of non-additive costs.

The paper introduces PRAXIS, a novel algorithm that efficiently approximates the computation of 'Rashomon sets' for decision trees, significantly reducing memory and runtime complexity.

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 proposes Fuzzy-Function Programming and introduces Program-as-Weights (PAW), a compact, locally-executable neural artifact for everyday programming tasks.

This paper introduces an activation hierarchy and proves a Predictor-Impossibility Theorem, showing that no effective predictor family can determine all stage languages. It also establishes a slice th…

KoAT is a tool that automatically infers complexity bounds and proves termination of integer programs using an alternating modular analysis approach and a portfolio of techniques.

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

The paper introduces Grid Programs, a novel, Turing-complete model of computation where programs are two-dimensional arrangements of instructions, fundamentally departing from linear code structures.

FPMoE introduces a sparse Mixture-of-Experts (MoE) architecture to improve functional code generation across multiple functional programming languages, achieving state-of-the-art performance with fewe…

This paper introduces a framework for certifying the reliability of stochastic oracles and derives bounds on the minimum expected token cost for reliable oracle certification.

This paper investigates the tradeoff between parameter estimation and predictive accuracy in probabilistic temporal graphs, proposing a causal framework for evaluation.