Search Results for author: Rong Ma

Found 13 papers, 4 papers with code

Narrative Detection and Feature Analysis in Online Health Communities

no code implementations NAACL (WNU) 2022 Achyutarama Ganti, Steven Wilson, Zexin Ma, Xinyan Zhao, Rong Ma

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.

text-classification Text Classification

Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data

1 code implementation3 Aug 2023 Rong Ma, Eric D. Sun, David Donoho, James Zou

SMAI provides a statistical test to robustly determine the alignability between datasets to avoid misleading inference, and is justified by high-dimensional statistical theory.

Data Integration Imputation

A Spectral Method for Assessing and Combining Multiple Data Visualizations

1 code implementation25 Oct 2022 Rong Ma, Eric D. Sun, James Zou

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.}

Data Visualization Dimensionality Reduction

Using Interpretable Machine Learning to Massively Increase the Number of Antibody-Virus Interactions Across Studies

no code implementations10 Jun 2022 Tal Einav, Rong Ma

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.

Interpretable Machine Learning

BARS: Towards Open Benchmarking for Recommender Systems

3 code implementations19 May 2022 Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang

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.

Benchmarking Recommendation Systems

Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach

no code implementations28 Feb 2022 Xiucai Ding, Rong Ma

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.

Data Visualization

Matrix Reordering for Noisy Disordered Matrices: Optimality and Computationally Efficient Algorithms

no code implementations17 Jan 2022 T. Tony Cai, Rong Ma

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

1 code implementation7 Jun 2021 Fei Xue, Rong Ma, Hongzhe Li

Blockwise missing data occurs frequently when we integrate multisource or multimodality data where different sources or modalities contain complementary information.

Imputation regression

Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data

no code implementations16 May 2021 T. Tony Cai, Rong Ma

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.

Clustering Data Visualization +2

Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression

no code implementations6 Nov 2020 Linjun Zhang, Rong Ma, T. Tony Cai, Hongzhe Li

Based on the iterative estimators, we further construct debiased estimators and establish their asymptotic normality.


Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

no code implementations18 Feb 2020 T. Tony Cai, Hongzhe Li, Rong Ma

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

no code implementations11 Dec 2017 Ying Zeng, Yansong Feng, Rong Ma, Zheng Wang, Rui Yan, Chongde Shi, Dongyan Zhao

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.

Event Extraction

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