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08/02/2023
Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities
New technologies have led to vast troves of large and complex datasets a...
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05/22/2023
Nonparanormal Graph Quilting with Applications to Calcium Imaging
Probabilistic graphical models have become an important unsupervised lea...
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04/18/2023
High-dimensional Multi-class Classification with Presence-only Data
Classification with positive and unlabeled (PU) data frequently arises i...
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10/20/2022
Graphical Model Inference with Erosely Measured Data
In this paper, we investigate the Gaussian graphical model inference pro...
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09/17/2022
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
As a tool for estimating networks in high dimensions, graphical models a...
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06/05/2022
Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance
In order to trust machine learning for high-stakes problems, we need mod...
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11/19/2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits
Stochastic gradient descent (SGD) and its variants have established them...
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10/06/2020
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
This paper studies a general framework for high-order tensor SVD. We pro...
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03/16/2020
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions
High-dimensional autoregressive point processes model how current events...
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12/10/2018
Testing for high-dimensional network parameters in auto-regressive models
High-dimensional auto-regressive models provide a natural way to model i...
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12/10/2018