Search Results for author:
Found 9 papers, 6 papers with code
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Hence, there are often limits on how much-labeled data is available for training.
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
This paper addresses the task of contextual translation using multi-segment models.
Fair-SSL: Building fair ML Software with less data
Semi-supervised learning is a machine learning technique where, incrementally, labeled data is used to generate pseudo-labels for the rest of the data (and then all that data is used for model training).
Fair Enough: Searching for Sufficient Measures of Fairness
In summary, to simplify the fairness testing problem, we recommend the following steps: (1)~determine what type of fairness is desirable (and we offer a handful of such types); then (2) lookup those types in our clusters; then (3) just test for one item per cluster.
Bias in Machine Learning Software: Why? How? What to do?
This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples.
Early Life Cycle Software Defect Prediction. Why? How?
Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else.
Revisiting Process versus Product Metrics: a Large Scale Analysis
We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large.
Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)
To the best of our knowledge, STABILIZER is order of magnitude faster than the prior state-of-the-art transfer learners which seek to find conclusion stability, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature.
500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)
Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results.
