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A Spectral Framework for Closed-Form Relative Density Estimation

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation

Scaling Limits of Long-Context Transformers

A Note on Non-Negative $L_1$-Approximating Polynomials

Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning

Linear Response Estimators for Singular Statistical Models

Statistical inference with belief functions: A survey

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Adaptive auditing of AI systems with anytime-valid guarantees

Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions

Risk-Controlled Post-Processing of Decision Policies

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A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment

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Optimal Confidence Band for Kernel Gradient Flow Estimator

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Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees

Information-theoretic Limits of Learning and Estimation

Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning

Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points

Causal discovery under mean independence and linearity

Perturbation is All You Need for Extrapolating Language Models

Realizable Bayes-Consistency for General Metric Losses

Vanishing L2 regularization for the softmax Multi Armed Bandit

Imbalanced Classification under Capacity Constraints

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On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization

Extrapolation in Statistical Learning with Extreme Value Theory

Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality

Beyond ECE: Calibrated Size Ratio, Risk Assessment, and Confidence-Weighted Metrics

Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression

Mean Testing under Truncation beyond Gaussian

Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport

Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time

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Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions

Concave Statistical Utility Maximization Bandits via Influence-Function Gradients

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Cover meets Robbins while Betting on Bounded Data: $\ln n$ Regret and Almost Sure $\ln\ln n$ Regret

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ADD for Multi-Bit Image Watermarking

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Query Lower Bounds for Diffusion Sampling

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The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence

Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data

Conformal Policy Control

Continuous-time reinforcement learning: ellipticity enables model-free value function approximation

High-accuracy sampling for diffusion models and log-concave distributions

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Optimal Lower Bounds for Online Multicalibration

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Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks

Optimal In-context Adaptivity and Distributional Robustness of Transformers

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Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards

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Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition

Sharp Gaussian approximations for Decentralized Federated Learning

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels

Smoothed Analysis of Learning from Positive Samples

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