Identification of Genetic Factors Associated with Corpus Callosum Morphology: Conditional Strong Independence Screening for Non-Euclidean Responses

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

Variance Reduction for Expectations with Diffusion Teachers

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

Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference

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

The General Theory of Localization Methods

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

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

Tail Annealing for Heavy-Tailed Flow Matching

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation

Latent Laplace Diffusion for Irregular Multivariate Time Series

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

Reducing Diffusion Model Memorization with Higher Order Langevin Dynamics

Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming

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

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

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

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

Improved Baselines with Representation Autoencoders

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

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

A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions

Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space

Sample-efficient inductive matrix completion with noise and inexact side-information

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

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

TabPFN-3: Technical Report

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

Adaptive Kernel Density Estimation with Pre-training

Coreset-Induced Conditional Velocity Flow Matching

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

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

Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage

Online Learning-to-Defer with Varying Experts

Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification

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

One-Step Generative Modeling via Wasserstein Gradient Flows

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

A Composite Activation Function for Learning Stable Binary Representations

Adaptive Calibration in Non-Stationary Environments

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

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

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

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

Ensemble Distributionally Robust Bayesian Optimisation

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

Query-efficient model evaluation using cached responses

Order-Agnostic Autoregressive Modelling with Missing Data

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

Spherical Flows for Sampling Categorical Data

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

Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning

Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution

Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation

Understanding Self-Supervised Learning via Latent Distribution Matching

Imbalanced Classification under Capacity Constraints

Robust and Fast Training via Per-Sample Clipping

Efficient Preference Poisoning Attack on Offline RLHF

A Theory of Saddle Escape in Deep Nonlinear Networks

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

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

Conditional Score-Based Modeling of Effective Langevin Dynamics

Inference of Online Newton Methods with Nesterov's Accelerated Sketching

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

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

Learning to Emulate Chaos: Adversarial Optimal Transport Regularization

Geometric Layer-wise Approximation Rates for Deep Networks

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

Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training

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

Generative Augmented Inference

Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression

Rare Event Analysis via Stochastic Optimal Control

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

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

Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates

Feature Learning Dynamics in Infinite-Depth Neural Networks

Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing

Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions

Adversarial Robustness in One-Stage Learning-to-Defer

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

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

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

Random Walk Learning and the Pac-Man Attack

Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models

GradPower: Powering Gradients for Faster Language Model Pre-Training

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

Post-Training Augmentation Invariance

Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints

Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies

Program Evaluation with Remotely Sensed Outcomes

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series

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

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