Search Results for author:
Found 9 papers, 3 papers with code
Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representations
Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface.
Table-GPT: Table-tuned GPT for Diverse Table Tasks
Language models, such as GPT-3. 5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks.
Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples
Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases.
Auto-Validate by-History: Auto-Program Data Quality Constraints to Validate Recurring Data Pipelines
Data pipelines are widely employed in modern enterprises to power a variety of Machine-Learning (ML) and Business-Intelligence (BI) applications.
Ground Truth Inference for Weakly Supervised Entity Matching
We also show that a deep learning EM end model (DeepMatcher) trained on labels generated from our weak supervision approach is comparable to an end model trained using tens of thousands of ground-truth labels, demonstrating that our approach can significantly reduce the labeling efforts required in EM.
Auto-Tag: Tagging-Data-By-Example in Data Lakes
As data lakes become increasingly popular in large enterprises today, there is a growing need to tag or classify data assets (e. g., files and databases) in data lakes with additional metadata (e. g., semantic column-types), as the inferred metadata can enable a range of downstream applications like data governance (e. g., GDPR compliance), and dataset search.
Auto-Pipeline: Synthesizing Complex Data Pipelines By-Target Using Reinforcement Learning and Search
We in this work propose to automate multiple such steps end-to-end, by synthesizing complex data pipelines with both string transformations and table-manipulation operators.
Demonstration of Panda: A Weakly Supervised Entity Matching System
Panda's IDE includes many novel features purpose-built for EM, such as smart data sampling, a builtin library of EM utility functions, automatically generated LFs, visual debugging of LFs, and finally, an EM-specific labeling model.
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes
Complex data pipelines are increasingly common in diverse applications such as BI reporting and ML modeling.
