Joint Nuclear and $\ell_1$ Regularization for Logistic Matrix Regression with Applications to Brain Imaging

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

When Is Next-Token Prediction Useful? Marginalization, Ergodicity, Mixture Identifiability, Local Sufficiency, RAG, Tools, and Programming

Robust OT-Guided Generative Residual Domain Adaptation for Bike-Sharing Demand Prediction under Temporal Domain Shift

Any-Dimensional Invariant Universality

Instance-Optimal Estimation with Multiple LLM Judges on a Budget

Optimal Dimension-Free Sampling for Regularized Classification

Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries

Training-Free Looped Transformers

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics

LLM Sparsity Prior for Robust Feature Selection

Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models

Entropy Equivalence Testing

Dropout Universality: Scaling Laws and Optimal Scheduling at the Edge-of-Chaos

Symbolic Density Estimation for Discrete Distributions

Partial Fusion of Neural Networks: Efficient Tradeoffs Between Ensembles and Weight Aggregation

The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers

A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing

Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning

KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

Anytime Training with Schedule-Free Spectral Optimization

Human-Centered Learning Mechanics: A Dynamical Framework for Entropy-Regulated Representation Learning

The Matching Principle: A Geometric Theory of Loss Functions for Nuisance-Robust Representation Learning

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

Proxy-Based Approximation of Shapley and Banzhaf Interactions

Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels

CASCADE Conformal Prediction: Uncertainty-Adaptive Prediction Intervals for Two-Stage Clinical Decision Support

The General Theory of Localization Methods

Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification

CT-OT Flow: Estimating Continuous-Time Dynamics from Discrete Temporal Snapshots

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees

Adversarial Robustness in One-Stage Learning-to-Defer

Learning-to-Defer with Expert-Conditional Advice

Variance Reduction for Expectations with Diffusion Teachers

Scalable Reinforcement Learning via Adaptive Batch Scaling

TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design

A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions

Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming

Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation

Program Evaluation with Remotely Sensed Outcomes

Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity

Latent Laplace Diffusion for Irregular Multivariate Time Series

Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety

Sample efficient inductive matrix completion with noise and inexact side information

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent

On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions

Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization

Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad

Markov Chain Decoders Overcome the Heavy-Tail Limitations of Lipschitz Generative Models

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

Reasoning Models Don't Just Think Longer, They Move Differently

TabPFN-3: Technical Report

BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

Coreset-Induced Conditional Velocity Flow Matching

Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

Towards a holistic understanding of Selection Bias for Causal Effect Identification

One-Step Generative Modeling via Wasserstein Gradient Flows

Adaptive Calibration in Non-Stationary Environments

Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions

Online Learning-to-Defer with Varying Experts

RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty

Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage

Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation

Modulated learning for private and distributed regression with just a single sample per client device

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning

When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains

Spherical Flows for Sampling Categorical Data

Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes

Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors

Order-Agnostic Autoregressive Modelling with Missing Data

Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling

Query-efficient model evaluation using cached responses

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift

Understanding Self-Supervised Learning via Latent Distribution Matching

Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning

Efficient Preference Poisoning Attack on Offline RLHF

Adaptive Querying with AI Persona Priors

Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction

Electricity price forecasting across Norway's five bidding zones in the post-crisis era

Adversarial Robustness of NTK Neural Networks

Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces

Inference of Online Newton Methods with Nesterov's Accelerated Sketching

ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation

Post-Training Augmentation Invariance

S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection

Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

Rare Event Analysis via Stochastic Optimal Control

Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression

Generative Augmented Inference

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer

Feature Learning Dynamics in Infinite-Depth Neural Networks

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series

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