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Byzantine-Robust Distributed Sparse Learning Revisited

The Sample Complexity of Multiple Change Point Identification under Bandit Feedback

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Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces

MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound

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

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling

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

Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning

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

Time-Inhomogeneous Preconditioned Langevin Dynamics

A Fine-Grained Understanding of Uniform Convergence for Halfspaces

CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency

Ratio-based Loss Functions

Optimal Confidence Band for Kernel Gradient Flow Estimator

A renormalization-group inspired lattice-based framework for piecewise generalized linear models

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

The Sample Complexity of Multicalibration

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

Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model

On two ways to use determinantal point processes for Monte Carlo integration

Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights

Structural interpretability in SVMs with truncated orthogonal polynomial kernels

Cloning is as Hard as Learning for Stabilizer States

Ordinary Least Squares is a Special Case of Transformer

Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing

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

ADD for Multi-Bit Image Watermarking

Cost-optimal Sequential Testing via Doubly Robust Q-learning

Query Lower Bounds for Diffusion Sampling

Tail-Aware Information-Theoretic Generalization for RLHF and SGLD

Spatio-temporal probabilistic forecast using MMAF-guided learning

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

Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity

Optimal Lower Bounds for Online Multicalibration

Understanding Overparametrization in Survival Models through Interpolation

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

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

The Good, the Bad, and the Sampled: a No-Regret Approach to Safe Online Classification

GOSPA and T-GOSPA quasi-metrics for evaluation of multi-object tracking algorithms

A note on the unique properties of the Kullback--Leibler divergence for sampling via gradient flows

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards

Efficient compression of neural networks and datasets

Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective

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

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images

General Frameworks for Conditional Two-Sample Testing

Improved Hardness Results for Learning Intersections of Halfspaces

Consistency of Lloyd's Algorithm Under Perturbations

Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation

Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection

Efficient Parameter Estimation of Truncated Boolean Product Distributions

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