We prove a tight upper bound on the variance of the priority sampling me...
Recent advancements in software and hardware technologies have enabled t...
We present a new approach for computing compact sketches that can be use...
Given the massive growth in the volume of spatial data, there is a great...
We introduce AlphaD3M, an automatic machine learning (AutoML) system bas...
As data is a central component of many modern systems, the cause of a sy...
The increasing availability of structured datasets, from Web tables and
...
Machine Learning models are increasingly being adopted in many applicati...
The use of Automated Machine Learning (AutoML) systems are highly open-e...
In recent years, a wide variety of automated machine learning (AutoML)
m...
Data analysis for scientific experiments and enterprises, large-scale
si...
The availability of low cost sensors has led to an unprecedented growth ...
Machine learning tasks entail the use of complex computational pipelines...
We propose PODS (Predictable Outliers in Data-trendS), a method that, gi...
As a human choosing a supervised learning algorithm, it is natural to be...
While the demand for machine learning (ML) applications is booming, ther...
Automatic machine learning is an important problem in the forefront of
m...
Fake news and misinformation have been increasingly used to manipulate
p...
The ability to continuously discover domain-specific content from the We...
Reproducibility in the computational sciences has been stymied because o...
Although a standard in natural science, reproducibility has been only
ep...