Search Results for author:
Found 10 papers, 6 papers with code
Boosting Summarization with Normalizing Flows and Aggressive Training
This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization.
Boosting Data Analytics With Synthetic Volume Expansion
This article explores the effectiveness of statistical methods on synthetic data and the privacy risks of synthetic data.
Causal Discovery with Generalized Linear Models through Peeling Algorithms
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data.
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty Quantification
The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretation
We apply the proposed method to the MNIST dataset and the MIT-BIH dataset with a convolutional auto-encoder.
Distribution-Invariant Differential Privacy
This phenomenon is known as the trade-off between privacy protection and statistical accuracy.
Two-level monotonic multistage recommender systems
In this article, we develop a multistage recommender system utilizing a two-level monotonic property characterizing a monotonic chain of events for personalized prediction.
Significance tests of feature relevance for a black-box learner
In this article, we derive one-split and two-split tests relaxing the assumptions and computational complexity of existing black-box tests and extending to examine the significance of a collection of features of interest in a dataset of possibly a complex type such as an image.
Multilayer tensor factorization with applications to recommender systems
Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence.
Fused Multiple Graphical Lasso
We expect the two brain networks for NC and MCI to share common structures but not to be identical to each other; similarly for the two brain networks for MCI and AD.
