3 citations · 7 across the 13 of their papers we have counts for
13 papers
A Unified Framework for In-Context Learning with Causal and Masked Language Models
Chenrui Liu, Chuanlong Xie, Falong Tan +2
In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causal language models trained by…
Asymptotic Distribution-Free Tests for Ultra-high Dimensional Parametric Regressions via Projected Empirical Processes and -value Combination
Falong Tan, Shan Tang, Lixing Zhu
This paper develops a novel methodology for testing the goodness-of-fit of sparse parametric regression models based on projected empirical processes and p-value combination, where…
In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality
Chenrui Liu, Falong Tan, Chuanlong Xie +2
This paper investigates the expected excess risk of in-context learning (ICL) for multiclass classification. We formalize each task as a sequence of labeled examples followed by a…
A Two-Step Projection-Based Goodness-of-Fit Test for Ultra-High Dimensional Sparse Regressions
Falong Tan, Jie Liu, Heng Peng +1
This paper proposes a novel two-step strategy for testing the goodness-of-fit of parametric regression models in ultra-high dimensional sparse settings, where the predictor dimensi…
Variable Selection for Multi-Source Count Data with Controlled False Discovery Rate
Shan Tang, Shanjun Mao, Shourong Ma +1
The rapid generation of complex, highly skewed, and zero-inflated multi-source count data poses significant challenges for variable selection, particularly in biomedical domains li…
LRBmat: A Novel Gut Microbial Interaction and Individual Heterogeneity Inference Method for Colorectal Cancer
Shan Tang, Shanjun Mao, Yangyang Chen +4
Many diseases are considered to be closely related to the changes in the gut microbial community, including colorectal cancer (CRC), which is one of the most common cancers in the…
Boosting Out-of-Distribution Detection with Multiple Pre-trained Models
Feng Xue, Zi He, Chuanlong Xie +2
Out-of-Distribution (OOD) detection, i.e., identifying whether an input is sampled from a novel distribution other than the training distribution, is a critical task for safely dep…
Testing the parametric form of the conditional variance in regressions based on distance covariance
Yue Hu, Haiqi Li, Falong Tan
In this paper, we propose a new test for checking the parametric form of the conditional variance based on distance covariance in nonlinear and nonparametric regression models. Inh…
Weighted residual empirical processes, martingale transformations, and model specification tests for regressions with diverging number of parameters
Falong Tan, Xu Guo, Lixing Zhu
This paper explores hypothesis testing for the parametric forms of the mean and variance functions in regression models under diverging-dimension settings. To mitigate the curse of…
Integrated conditional moment test and beyond: when the number of covariates is divergent
Falong Tan, Lixing Zhu
The classic integrated conditional moment test is a promising method for testing regression model misspecification. However, it severely suffers from the curse of dimensionality. T…
Testing heteroscedasticity for regression models based on projections
Falong Tan, Xuejun Jiang, Xu Guo +1
In this paper we propose a new test of heteroscedasticity for parametric regression models and partial linear regression models in high dimensional settings. When the dimension of…
Estimation and adaptive-to-model testing for regressions with diverging number of predictors
Falong Tan, Lixing Zhu
The research described in this paper is motivated by model checking for parametric single-index models with diverging number of predictors. To construct a test statistic, we first…
A projection-based adaptive-to-model test for regressions
Falong Tan, Xuehu Zhu, Lixing Zhu
A longstanding problem of existing empirical process-based tests for regressions is that when the number of covariates is greater than one, they either have no tractable limiting n…