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Found 8 papers, 5 papers with code
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning
Finding errors in machine learning applications requires a thorough exploration of their behavior over data.
Rectifying Group Irregularities in Explanations for Distribution Shift
It is well-known that real-world changes constituting distribution shift adversely affect model performance.
Do Machine Learning Models Learn Statistical Rules Inferred from Data?
Test-time adaptation reduces these violations by up to 68. 7% with relative performance improvement up to 32%.
Learning to Select Pivotal Samples for Meta Re-weighting
In this paper, we study how to learn to identify such a meta sample set from a large, imperfect training set, that is subsequently cleaned and used to optimize performance in the meta re-weighting setting.
CHEF: A Cheap and Fast Pipeline for Iteratively Cleaning Label Uncertainties (Technical Report)
First, to reduce the cost of human annotators, we use Infl, which prioritizes the most influential training samples for cleaning and provides cleaned labels to save the cost of one human annotator.
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
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 applications.
DeltaGrad: Rapid retraining of machine learning models
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.
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models
The ubiquitous use of machine learning algorithms brings new challenges to traditional database problems such as incremental view update.
