36 citations · 72 across the 7 of their papers we have counts for
7 papers
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Aaditya Naik, Yinjun Wu, Mayur Naik +1
Machine learning models can make critical errors that are easily hidden within vast amounts of data. Such errors often run counter to rules based on human intuition. However, rules…
Learning to Select Pivotal Samples for Meta Re-weighting
Yinjun Wu, Adam Stein, Jacob Gardner +1
Sample re-weighting strategies provide a promising mechanism to deal with imperfect training data in machine learning, such as noisily labeled or class-imbalanced data. One such st…
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)
Yinjun Wu, James Weimer, Susan B. Davidson
High-quality labels are expensive to obtain for many machine learning tasks, such as medical image classification tasks. Therefore, probabilistic (weak) labels produced by weak sup…
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Yinjun Wu, Jingchao Ni, Wei Cheng +7
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging…
DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, Susan B. Davidson
Machine learning models are not static and may need to be retrained on slightly changed datasets, for instance, with the addition or deletion of a set of data points. This has many…
Lessons learned from the early performance evaluation of Intel Optane DC Persistent Memory in DBMS
Yinjun Wu, Kwanghyun Park, Rathijit Sen +2
Non-volatile memory (NVM) is an emerging technology, which has the persistence characteristics of large capacity storage devices(e.g., HDDs and SSDs), while providing the low acces…