This paper establishes a large deviation principle for the generalization error of interpolating classifiers in the overparametrized regime.

This paper develops a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms.

This paper proposes a randomized iterative method called Sequential Preconditioned Conjugate Gradient Method (SPCG) for large-scale linear statistical models, which significantly reduces computational…

This paper derives deterministic equivalents for the prediction risk of over-parameterized linear regression with degenerate covariance matrices and dependent covariates, and identifies the configurat…

This paper analyzes the statistical and computational limits of learning bounded linear operators between Sobolev spaces from noisy data, and constructs a finite-resolution blockwise least-squares est…

This paper proposes a two-stage clustering algorithm to ensure per-sample guardrails in LLM-based applications, reducing inference cost and latency.

This paper introduces Distributional Random Forests, which replace mean-based CART splitting with criteria that compare full conditional response distributions in candidate children. The authors syste…

This paper develops a unified spectral analysis framework to explain how knowledge transfer (KT) works across different machine learning regimes, such as Knowledge Distillation and Weak-to-Strong gene…

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.

InfoAtlas is a foundation model that estimates statistical mutual information (MI) in a single forward pass, achieving state-of-the-art accuracy with a massive speedup compared to traditional iterativ…

The paper proposes methodologies to measure lag relevance in machine learning forecasting models using Ghost variables, Shapley values, and additive importance measures. It also introduces auto-releva…

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