3 citations · 8 across the 14 of their papers we have counts for
15 papers
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…
Cluster Quilting: Spectral Clustering for Patchwork Learning
Lili Zheng, Andersen Chang, Genevera I. Allen
Patchwork learning arises as a new and challenging data collection paradigm where both samples and features are observed in fragmented subsets. Due to technological limits, measure…
Joint Semi-Symmetric Tensor PCA for Integrating Multi-modal Populations of Networks
Jiaming Liu, Lili Zheng, Zhengwu Zhang +1
Multi-modal populations of networks arise in many scenarios including in large-scale multi-modal neuroimaging studies that capture both functional and structural neuroimaging data…
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…
Nonparanormal Graph Quilting with Applications to Calcium Imaging
Andersen Chang, Lili Zheng, Gautam Dasarthy +1
Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functiona…
High-dimensional Multi-class Classification with Presence-only Data
Lili Zheng, Garvesh Raskutti
Classification with positive and unlabeled (PU) data frequently arises in bioinformatics, clinical data, and ecological studies, where collecting negative samples can be prohibitiv…
Graphical Model Inference with Erosely Measured Data
Lili Zheng, Genevera I. Allen
In this paper, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data.…
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
Andersen Chang, Lili Zheng, Genevera I. Allen
As a tool for estimating networks in high dimensions, graphical models are commonly applied to calcium imaging data to estimate functional neuronal connectivity, i.e. relationships…
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…
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
Yuchen Zhou, Anru R. Zhang, Lili Zheng +1
This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estim…
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…
Testing for high-dimensional network parameters in auto-regressive models
Lili Zheng, Garvesh Raskutti
High-dimensional auto-regressive models provide a natural way to model influence between actors given multi-variate time series data for time intervals. While there has bee…