Forecasting the U.S. Treasury Yield Curve: A Distributionally Robust Machine Learning Approach for Interest Rate Risk Management

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

Any-Dimensional Invariant Universality

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

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

Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning

Anytime Training with Schedule-Free Spectral Optimization

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

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

GradPower: Powering Gradients for Faster Language Model Pre-Training

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

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

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

Improved Baselines with Representation Autoencoders

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

TabPFN-3: Technical Report

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

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

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

Adaptive Kernel Density Estimation with Pre-training

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 Calibration in Non-Stationary Environments

A Composite Activation Function for Learning Stable Binary Representations

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

Online Learning-to-Defer with Varying Experts

Neural ARFIMA model for forecasting BRIC exchange rates with long memory

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

One-Step Generative Modeling via Wasserstein Gradient Flows

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

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

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

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

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

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

Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift

Spherical Flows for Sampling Categorical Data

Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics

Understanding Self-Supervised Learning via Latent Distribution Matching

Imbalanced Classification under Capacity Constraints

Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation

Efficient Preference Poisoning Attack on Offline RLHF

Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction

Adaptive Querying with AI Persona Priors

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

Conditional Score-Based Modeling of Effective Langevin Dynamics

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

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

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

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

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

Program Evaluation with Remotely Sensed Outcomes

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

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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