Another Look at Log-PCA for Probability Measures: A Dynamical Formulation and Statistical Convergence

A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning

Non-asymptotic Tail Bounds for the Kostlan--Shub--Smale Field: Tensor PCA and Spherical $k$-Spin Complexity

Conformal Prediction Intervals with Tail-Specific Guarantees

Tight $L_\infty$ Sample Complexity for Low-Degree and Sparse Boolean Polynomials

Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty

A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer

MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts

LLMs on Tabular Data with Limited Semantics: Evidence from Industrial Car Retrofit Prediction

Service-Induced Congestion in Memory-Constrained LLM Serving

Proximal Policy Optimization for Amortized Discrete Sampling

Topological Flow Matching

Attention is Just Another Name for Coupling?: A Fast-Slow ODE Perspective on Hierarchical Pretraining

Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data

Learning a Sampling-Free Variational DNN Plugin from Tiny Training Sets to Refine OOD Segmentation With Uncertainty Estimation

Exact Posterior Score Estimation for Solving Linear Inverse Problems

Bridging data-driven priors via the score function for posterior sampling -- Comparative review and experimental study

Audited Conformal Prediction for Classification under Unknown Distribution Shift

A Koopman-PINN Framework for Epidemic Models: Parameter Inference and Forecasting

Conformal Candidate Certification for Offline Model-Based Optimization

Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces

The Reverse Telescoping Coordinate System for Positive Definite Matrices: Geometry, Computation, and Generative Modeling

Structured Nonparametric Variational Inference for Dependent Latent Modeling

Ricci-Filtration: Boosting Retrieval-Augmented Generation Reranker to Query-Answer Tasks by Discrete Ricci Flow

Phase Transition in Convex Relaxations for Graph Alignment

Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures

Stochastic trace estimation with tensor train random vectors

Score-Based Martingale Posteriors for Deep Neural Networks

Amortized mean-shift interacting particles

Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data

Representation Costs in Data Science: Foundations and the Quasi-Banach Spaces of Deep Neural Networks

PromptShift-CRC: Drift-Aware Conformal Risk Control for Foundation Models Under Prompt and Domain Shift

Closing the Approximation Gap in Simulation-free Latent SDEs

On the Geometry of Separation in Finite Gaussian Mixtures

Generative Modeling on Metric Graphs via Neural Optimal Transport

Diffusion Flow Matching: Dimension-Improved KL Bounds and Wasserstein Guarantees

Spectral Sparsification of Laplacian-Constrained Gaussian and Hüsler-Reiss Graphical Models

Optimal Multiscale Learning of Linear Operators

A nonparametric two-sample test using a parametric integral probability metric

Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy

Dynestyx: A Probabilistic Programming Library for Dynamical Systems

Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis

Learning Topological Representations for Molecular Dynamics

Event Generation with Parallel Langevin Sampling and Learned Stein Diagnostics

PHINN: Persistent Homology Inspired Neural Network for Rare-Event Time Series Generation

A Decision-Theoretic View of Test-Time Training: When, How Far, and Which Directions to Adapt

The Data Manifold under the Microscope

The limits of interpretability in multiple linear regression

Stop the Sampler! Classifier-Based Adaptive Stopping for Sampling Kernels

One-Step Generalization Ratio Guided Optimization for Domain Generalization

Scalable and Interpretable Representation Alignment with Ordinal Similarity

Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier

Generative Predictive Distributions for Time Series

Functional Gradient Descent with Adaptive Representations

Filtered Conformal Ellipsoids for Graph-Native Time Series

Information Leakage Detection through Approximate Bayes-optimal Prediction

TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression

Optimality in importance sampling: a gentle survey

Discrimination-free Insurance Pricing with Privatized Sensitive Attributes

Enhancing Visual Feature Attribution via Weighted Integrated Gradients

Optimal structure learning and conditional independence testing

Kernel Two-Sample Testing via Directional Components Analysis

Q-Learning with Fine-Grained Gap-Dependent Regret

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

deFOREST: Fusing Optical and Radar satellite data for Enhanced Sensing of Tree-loss

Matching correlated VAR time series

Drivers, Receivers, and Dynamic Linkages: The Directed Structure of SDG Interdependence, 2000--2024

Best Arm Identification with Minimal Regret

Constraining the outputs of ReLU neural networks

On the Benefits of Weight Normalization for Overparameterized Matrix Sensing

Localized Kernel Projection Outlyingness: A Two-Stage Approach for Multi-Modal Outlier Detection

Random Gradient-Free Optimization in Infinite Dimensional Spaces

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

Fast Non-Episodic Finite-Horizon RL with K-Step Lookahead Thresholding

Sharp analysis of linear ensemble sampling

CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization

Sharp One-Dimensional Sub-Gaussian Comparison in Convex Order

Lyapunov-Based Sample Complexity Analysis for Weakly-Coupled MDPs

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

Relational Structural Causal Models

Simultaneous Latent Budget Trees for Stratified Classification

Asymptotically Optimal Sequential Testing with Markovian Data

High-Dimensional Robust Change-Point Detection via Angular Kernel Statistics

GradPower: Powering Gradients for Faster Language Model Pre-Training

Improved Baselines with Representation Autoencoders

Adaptive Kernel Density Estimation with Pre-training

Imbalanced Classification under Capacity Constraints

Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation

Conditional Score-Based Modeling of Effective Langevin Dynamics

A Unified Definition of Hallucination: It's The World Model, Stupid!

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

Semantic Editing with Coupled Stochastic Differential Equations

Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations

Optimal Transport for Machine Learners

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

Golden Ratio Weighting Prevents Model Collapse

Computational Safety for Generative AI: A Hypothesis Testing Perspective

DAL: A Practical Prior-Free Black-Box Framework for Piecewise Stationary Bandits

Conditional Local Importance by Quantile Expectations

Canonical Variates in Wasserstein Metric Space

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