Search Results for author:
Found 19 papers, 6 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.
Optimal Estimation of Shared Singular Subspaces across Multiple Noisy Matrices
Estimating singular subspaces from noisy matrices is a fundamental problem with wide-ranging applications across various fields.
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective
LOO-map extends the embedding over a discrete set of input points to the entire input space, enabling a systematic assessment of map continuity, and thus the reliability of the visualizations.
Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs
This enables semantic segmentation models to be trained simultaneously on multiple datasets, resulting in performance improvements.
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis.
Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation
As such, LDEs have to be faithful to the original high-dimensional data, i. e., they should represent the relationships that are encoded in the data, both at a local as well as global scale.
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
Integrative analysis of multiple heterogeneous datasets has become standard practice in many research fields, especially in single-cell genomics and medical informatics.
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.
