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20052026
most citedHigh-dimensional classification using features annealed independence rules

496 citations · 1.9k across the 52 of their papers we have counts for

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15 papers · 1 filter

stat.ML2026

CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk

Tianxing Mei, Yingying Fan, Mingming Leng +1

CART random forests are among the most widely used modern predictive methods, with well-documented empirical success. Yet, at the mechanistic level, the algorithm is often treated…

stat.ML2026

Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning

Yingying Fan, Yuxuan Han, Jinchi Lv +2

We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is , with an unknown utility map a…

stat.ML2025

MOSAIC: Minimax-Optimal Sparsity-Adaptive Inference for Change Points in Dynamic Networks

Yingying Fan, Jingyuan Liu, Jinchi Lv +1

We propose a new inference framework, named MOSAIC, for change-point detection in dynamic networks with the simultaneous low-rank and sparse-change structure. We establish the mini…

stat.ML2025

LLM-Powered CPI Prediction Inference with Online Text Time Series

Yingying Fan, Jinchi Lv, Ao Sun +1

Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the rece…

stat.ML2025

Asymptotic Theory of Eigenvectors for Latent Embeddings with Generalized Laplacian Matrices

Jianqing Fan, Yingying Fan, Jinchi Lv +2

Laplacian matrices are commonly employed in many real applications, encoding the underlying latent structural information such as graphs and manifolds. The use of the normalization…

stat.ML2025

Asymptotic FDR Control with Model-X Knockoffs: Is Moments Matching Sufficient?

Yingying Fan, Lan Gao, Jinchi Lv +1

We propose a unified theoretical framework for studying the robustness of the model-X knockoffs framework by investigating the asymptotic false discovery rate (FDR) control of the…