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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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Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

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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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

Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy

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

Elite-Driven Support Vector Machines for Classification

A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws

Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions

Concave Statistical Utility Maximization Bandits via Influence-Function Gradients

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ADD for Multi-Bit Image Watermarking

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Query Lower Bounds for Diffusion Sampling

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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

Efficient compression of neural networks and datasets

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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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