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Found 10 papers, 7 papers with code
CleanAgent: Automating Data Standardization with LLM-based Agents
Data standardization is a crucial part in data science life cycle.
FeatAug: Automatic Feature Augmentation From One-to-Many Relationship Tables
To overcome this limitation, we propose FEATAUG, a new feature augmentation framework that automatically extracts predicate-aware SQL queries from one-to-many relationship tables.
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
This observation enables us to extend a variety of HPO and NAS algorithms to solve the Auto-FP problem.
Enabling SQL-based Training Data Debugging for Federated Learning
The SQL-based training data debugging framework has proved effective to fix this kind of issue in a non-federated learning setting.
Explaining Inference Queries with Bayesian Optimization
BO - a technique for finding the global optimum of a black-box function - is used to find the best predicate.
Are We Ready For Learned Cardinality Estimation?
The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs.
Complaint-driven Training Data Debugging for Query 2.0
As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2. 0", which integrates model inference into SQL queries.
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.
Crowd-Powered Data Mining
Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification.
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Data cleaning is often an important step to ensure that predictive models, such as regression and classification, are not affected by systematic errors such as inconsistent, out-of-date, or outlier data.
