Conditional sampling via diffusion flow and SMC

Adaptive Multi-task Learning for Multi-sector Portfolio Optimization

Optimizing Dynamic Treatment Regimes under Spatial Interference: Evidence from COVID-19 School Closures

Digital behaviors signal consumer well-being: A factor-augmented regularized prediction model approach

MR2G: A novel framework for causal network inference using GWAS summary

A Statistical Framework for Alignment with Biased AI Feedback

Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs

Efficient machine unlearning with minimax optimality

Anti-Concentration Inequalities for the Difference of Maxima of Gaussian Random Vectors

Federated Transfer Learning with Differential Privacy

Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals

Optimality Theory for Adaptation under Differential Privacy

Confounder-robust causal discovery and inference in Perturb-seq using proxy and instrumental variables

Robust Bias Calibration for Causal Inference of Observational Data

Correcting Nonignorable Nonresponse Bias in Turnout Estimation Using Callback Data

Identification and Robust Inference for Multiple Treatments with Possibly Invalid Instruments

Active Hypothesis Testing under Computational Budgets

Power Enhancement for Test of Multivariate Independence

Model-X Knockoff Framework for Genome-Wide Survival Association Analysis

Is the F-test doubly robust?

LLM-Powered Deep Panel Modeling

Statistical Inference for Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss

Variational Bayes for high-dimensional structured mixture model‌

Shape-Adaptive Conformal Prediction with Conditional Validity via Minimax Optimization

Towards Understanding How Transformers Perform In-Context Logistic Regression

Non-vacuous Generalization Bounds for Overparameterized Shallow Neural Networks

Transformers Meet In-Context Learning: A Universal Approximation Theory

Spectral Gradient Descent Mitigates Anisotropy-Driven Misalignment: A Case Study in Phase Retrieval

Machine learning methods to predict amyloid positivity using domain scores from cognitive tests

Statistical methods for cell-cell interaction studies on spatially resolved transcriptomics

Forecasting Respiratory Infectious Diseases with Adaptive and Physics-Informed Machine Learning

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research

Rate-Multiply Robust Estimation of General Treatment Models via Balanced Weighting

Perturbed Double Machine Learning: Nonstandard Inference Beyond the Parametric Length

Towards an efficiency theory on parameter manifolds

TBD

Latent Space Modeling for Human Disease Network with Temporal Variations: Analysis of Medicare Data

Causal effect heterogeneity estimation using summary statistics

Statistical Inference for Random Objects

High-dimensional Inference for Extreme Value Indices

Asymptotic Theory and Penalized Estimation for Signal-plus-Noise Matrices with Heteroskedastic Noise and Weak Signals

Probabilistic PCA on tensors

Wasserstein Generative Regression

A robust and scalable framework for high-dimensional volatility estimation

High-dimensional autoregressive time series modeling for symmetric matrices

High-dimensional Autoregressive Modeling for Time Series Data with Hierarchical Structures

Quantifying Cross-Domain Knowledge Distillation in the Presence of Domain shift

Geometric Fluctuations of Principal Subspaces for High-Dimensional Covariance Matrices

Stance Drift: How AI-Mediated Communication Distorts Our Message

CalCS: Calibrated Cost-Sensitive Classification under Strict Error Constraints

Federated Learning of Quantile Inference under Local Differential Privacy

Minimax and Adaptive Covariance Matrix Estimation under Differential Privacy

Communication-efficient Distributed Statistical Analysis under Differential Privacy

Balancing utility and cost in dynamic treatment regimes

Constructive Instrumental Variable Identification and Inference with Many Weak Interaction Moments

Words matter: Multimodal Suicide Risk Prediction from Veterans Health Administration Clinical Notes

Deciphering microbial community dynamics using cross-sectional data-informed NeuralODE

Distributed Learning with Heterogeneity and Asynchrony: Representative Learning

Cross-Sectional Learning and Inference for the Stochastic Discount Factor

Adaptive partial monitoring in non-stationary environments

Extremal eigenvectors of sparse random matrices

Optimal Inference of the Mean Outcome under Optimal Treatment Regime

Differentially private sliced inverse regression in the federated paradigm

Adapting to noise tails in private linear regression

Federated PCA: Differential Privacy, Algorithms, and Optimality under the Spiked Model

Weighted Youden Index Maximization

Provable imitation learning for nuclear fusion control

Stein-Encoder: A White-Box Supervised Encoder via Stein Identities in Multi-Modal Studies

Multi-Fidelity Quantile Regression

Boosting as Sequential Aggregation of Generators for Vector-Valued Prediction

New Frontiers on Performativity: Inference Theory

A Queueing-Theoretic Framework for LLM Inference with KV Cache Memory Constraints

TBD

Site Percolation Network Models for Event-Driven Systems

Cross-Semanic Transfer Learning for High-dimensional Linear Regression

Multi-Source Domain Adaptation via Alignment-Guided Distributionally Robust Learning

Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction

All-in-One Toolkit for Biobank-Scale Whole-Genome Sequencing Data Management and Analysis

A Powerful Transformation of Quantitative Responses for Biobank-Scale Association Studies

A Unified Mendelian Randomization Framework for Identifying Causal Risk Factors: Accounting for Measured Covariates and Unmeasured Confounders

Bridging unpaired single-cell multimodal data for integrative analyses with SuperMap

Computational and Statistical Asymptotic Analysis of the JKO Scheme with Unknown Parameters

Structural Change Detection in Dynamic Systems

A Distributional Measure of Heterogeneous Variable Importance

Opportunities and Challenges of Sharpness-Aware Training for Overparameterized Neural Networks

Asymptotics of higher criticism via Gaussian approximation

High-dimensional Clustering and Signal Recovery under Block Signals

Semiparametric Sieve Estimation for Survival Data with Two-layer Censoring

Granular Data: Laying a Solid Foundation for High-Quality Datasets, Empowering AI Applications

Unified Conformalized Multiple Testing with Full Data Efficiency

Gold after Randomization: Model-X Split Knockoffs for FDR Control in Transformation Selection

Multiple Testing Meets Data Visualization: A Modern Perspective on Boxplots and Bagplots

A New Approach to Conformalized Model Selection for Out-of Distribution Testing

Outrigger local polynomial regression

Policy Averaging for Stochastic Decision Problems: Theory and an Application to the Newsvendor Problem

Distributed propensity model averaging for large-scale data with nonignorable nonresponse

Double Descent and Emergence in Multi-model Prediction

Semi-supervised learning using copula-based regression and model averaging

Misspecified Maximum Likelihood Estimation for Non-uniform Group Orbit Recovery

Stable Gaussian Mixture Black-Box Variational Inference

Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization

Transfer learning in nonparametric online learning problems

Locally Private Estimation with Public Features

Second-Order Sparse Sufficient Dimension Reduction with Applications to Quadratic Discriminant Analysis

Metric conformal prediction based on the expected local radius

Combining pre-trained large models via localized model averagin

Neural Wasserstein Two-Sample Tests

Ball Impurity: Measuring Heterogeneity in General Metric Spaces

Semi-Supervised Generative Learning via Latent Space Distribution Matching

Factor-augmented clustering tree for time series

Slacked Empirical Likelihoods for Post-Criterion Inference

Bayesian reinforcement learning framework for optimizing the BCI-utility of P300 Brain-Computer Interfaces

Online Sequential Decision-Making with Reinforcement Learning: From Robotics to Human-centered tasks

Inference on Large-scale Partially Functional Linear Model with Heterogeneous Errors

Heritability Estimation via Genetic Similarity Representation: Theory and Biobank-Scale Computation

Generalizing Experience for Language Agents with Hierarchical MetaFlows

Conformal Inference for Minority Subgroups via Cross-Group Borrowing

Design-based inference for edge-level outcomes in directed networks

Inference-Time Alignment for Diffusion Models via Variationally Stable Doob’s Matching

Heavy-tailed Information-Theoretic Generalization Bounds with Applications to LLM Safety Alignment

Symmetry in Deep Neural Networks and Implications to Learning

A novel statistical approach to analyze image classification

TBD

Doubly Robust Estimators for Heterogeneous Treatment Effects in Heteroskedastic Survival Data and Application

Distributed Censored Quantile Regression: Convolution Smoothing and Communication Efficiency

Dynamic-Centime：一种利用纵向数据预测剩余生存时间的动态预测模型

模拟学习方法论：理论、算法及应用

Strongly consistent community detection in popularity adjusted block models

Optimal Mixture-of-Experts Model Averaging for Conditional Generative Learning

Controlling the False Discovery Rate in High-Dimensional Linear Models Using Model-X Knockoffs and p-values

SMODER: Spatial Multi-Omics Deconvolution Anchored by RNA

Nonparametric Inference on Unlabeled Histograms with Application to Generative Uncertainty Evaluation

Provable RLHF: A Consistent Framework for Offline Reward Learning and Value Function Learning

Semi-supervised learning in high-dimensional linear regression

Identification and Inference for Structural Accelerated Failure Time Models via Instrument Interactions

Neural frailty machines for survival analysis

Variable Significance Testing for the Deep Cox Model

Deep partially linear transformation model for right-censored survival data

Towards Intelligent Story Visualization and Editing

Intelligent Design Generation: From SVG to Parametric CAD

Research on Data and Methods for Ultra-High-Definition Visual Generation

Multi-View 3D Reconstruction with Radiance Fields

Statistical ranking with dynamic covariates

Prediction-Oriented Transfer Learning for Survival Analysis

What Separates Useful from Useless Synthetic Data? Verifying Synthetic Data through Representations

Homogeneity Pursuit in Ranking Inference Based on Pairwise Comparison

Deep Learning Assisted Variable Selection with False Discovery Rate Control

Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes

Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making

Identifying the Desert Decision Rule to Assess and Achieve Fairness

AI for dynamics network biology

Local-and-Global Information-Preserving Statistical Manifold Learning for Single-Cell Transcriptomics

Deciphering Tissue Spatial Heterogeneity: Methods for the Identification of Spatially Variable Genes and the Characterization of Spatial Structures

Meta-Encoder: Integrating Multiple Pathological Foundation Models to Enhance Predictive Accuracy for Key Cancer Biomarkers and Spatial Omics

Towards 3D Content Generation and Understanding via Pretrained Generative Priors

Realistic Human Interaction in Virtual Environments

Visual Affective-Cognitive Perception and Modeling

TBD

The Evolution of Agents

Towards Efficient AI: Optimizing Deployment and Inference

Analogy, Abstraction, and Reasoning in Multimodal Large Models

Towards Efficient Inference of Large Foundation Models

Bias reduction in g-computation for covariate adjustment in randomized clinical trials

Higher-order debiased estimators of general treatment models

Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood

Incorporating external data for analyzing randomized clinical trials: A transfer learning approach

Common-Individual Embedding for Dynamic Networks with Temporal Group Structure

Latent Space Model under Edge Contamination

Likelihood-Based Change Point Detection for Sparse SBM Networks

Graphical Models for Mixed-type Data

Hierarchical Contrastive Learning for Multimodal Data with Partial Sharing

Semi-supervised Clustering Through Representation Learning of High-dimensional Count Data

Multi-view Spherical Mixture Models for Aligning Partially Overlapping Feature Spaces with Latent Synonymy

Efficient inference on high-dimensional logistic regression under class imbalance

Bias Correction for Semiparametric Regression Models

Studies in Label Shift

The implicit bootstrap: a percentile second-order correct interval estimation method

Partial optimality and applicability for multivariate equivalence testing