3 citations · 8 across the 14 of their papers we have counts for
7 papers · 1 filter
LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
Yinan Cheng, Lili Zheng
As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of int…
Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
Xuhui Liu, Lili Zheng
Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging. Ex…
iLOCO: Distribution-Free Inference for Feature Interactions
Camille Little, Lili Zheng, Genevera Allen
Feature importance measures are widely studied and are essential for understanding model behavior, guiding feature selection, and enhancing interpretability. However, many machine…
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities
Genevera I. Allen, Luqin Gan, Lili Zheng
New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only p…
LOCO Feature Importance Inference without Data Splitting via Minipatch Ensembles
Luqin Gan, Lili Zheng, Genevera I. Allen
Feature importance inference is critical for the interpretability and reliability of machine learning models. There has been increasing interest in developing model-agnostic approa…
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Hao Chen, Lili Zheng, Raed Al Kontar +1
Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to the…
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions
Lili Zheng, Garvesh Raskutti, Rebecca Willett +1
High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future…