1 citations · 5 across the 12 of their papers we have counts for
12 papers
Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints
Jiayu Li, Enpei Zhang, Dawei Zhou +2
We study workflow learning in a setting where specialized agents hand off control through a shared artifact, each agent observes only a local function of that artifact and its own…
Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection
Elynn Chen, Jiayu Li, Zheshi Zheng +1
Tensor-valued data arise naturally in neuroimaging, genomics, climate science, and spatiotemporal networks, where multilinear dependencies across modes carry information that is de…
Quantifying Epistemic Uncertainty in Diffusion Models
Aditi Gupta, Raphael A. Meyer, Yotam Yaniv +2
To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models. Existing methods are often unreliable because they mix epistemic and alea…
High-Dimensional Tensor Discriminant Analysis: Low-Rank Discriminant Structure, Representation Synergy, and Theoretical Guarantees
Elynn Chen, Yuefeng Han, Jiayu Li
High-dimensional tensor-valued predictors arise in modern applications, increasingly as learned representations from neural networks. Existing tensor classification methods rely on…
Tensor Neyman-Pearson Classification: Theory, Algorithms, and Error Control
Lingchong Liu, Elynn Chen, Yuefeng Han +1
Biochemical discovery increasingly relies on classifying molecular structures when the consequences of different errors are highly asymmetric. In mutagenicity and carcinogenicity,…
Modewise Additive Factor Model for Matrix Time Series
Elynn Chen, Yuefeng Han, Jiayu Li +1
We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offe…
Time-Varying Factor-Augmented Models for Volatility Forecasting
Duo Zhang, Jiayu Li, Junyi Mo +1
Accurate volatility forecasts are vital in modern finance for risk management, portfolio allocation, and strategic decision-making. However, existing methods face key limitations.…
ACT-Tensor: Tensor Completion Framework for Financial Dataset Imputation
Junyi Mo, Jiayu Li, Duo Zhang +1
Missing data in financial panels presents a critical obstacle, undermining asset-pricing models and reducing the effectiveness of investment strategies. Such panels are often inher…
Statistical Inference for Low-Rank Tensor Models
Ke Xu, Elynn Chen, Yuefeng Han
Statistical inference for tensors has emerged as a critical challenge in analyzing high-dimensional data in modern data science. This paper introduces a unified framework for infer…
High-Dimensional Tensor Discriminant Analysis with Incomplete Tensors
Elynn Chen, Yuefeng Han, Jiayu Li
Tensor classification is gaining importance across fields, yet handling partially observed data remains challenging. In this paper, we introduce a novel approach to tensor classifi…
TEAFormers: TEnsor-Augmented Transformers for Multi-Dimensional Time Series Forecasting
Linghang Kong, Elynn Chen, Yuzhou Chen +1
Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional…
High-Dimensional Tensor Classification with CP Low-Rank Discriminant Structure
Elynn Chen, Yuefeng Han, Jiayu Li
Tensor classification has become increasingly crucial in statistics and machine learning, with applications spanning neuroimaging, computer vision, and recommendation systems. Howe…