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Linear Response Estimators for Singular Statistical Models

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

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Optimal Confidence Band for Kernel Gradient Flow Estimator

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Information-theoretic Limits of Learning and Estimation

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

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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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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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Concave Statistical Utility Maximization Bandits via Influence-Function Gradients

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NetworkNet: A Deep Neural Network Approach for Random Networks with Sparse Nodal Attributes and Complex Nodal Heterogeneity

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

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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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