Search Results for author:
Found 13 papers, 4 papers with code
Narrative Detection and Feature Analysis in Online Health Communities
Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study health-related narratives in social media.
Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data
To overcome these limitations, we present a spectral manifold alignment and inference (SMAI) framework, which enables principled and interpretable alignability testing and structure-preserving integration of single-cell data with the same type of features.
A Spectral Method for Assessing and Combining Multiple Data Visualizations
Then it leverages the eigenscores to obtain a consensus visualization, which has much improved { quality over the individual visualizations in capturing the underlying true data structure.}
Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies
We use this framework to greatly expand seven influenza datasets utilizing hemagglutination inhibition, validating our method upon 200, 000 existing measurements and predicting 2, 000, 000 new values along with their uncertainties.
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach
We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dimensional manifold and corrupted by high-dimensional noise.
Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms
Motivated by applications in single-cell biology and metagenomics, we investigate the problem of matrix reordering based on a noisy disordered monotone Toeplitz matrix model.
Statistical Inference for High-Dimensional Linear Regression with Blockwise Missing Data
Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information.
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data
This paper investigates the theoretical foundations of the t-distributed stochastic neighbor embedding (t-SNE) algorithm, a popular nonlinear dimension reduction and data visualization method.
Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression
Based on the iterative estimators, we further construct debiased estimators and establish their asymptotic normality.
MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis
The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications.
Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Driven by a wide range of applications, many principal subspace estimation problems have been studied individually under different structural constraints.
Scale Up Event Extraction Learning via Automatic Training Data Generation
We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.
