99 citations · 166 across the 5 of their papers we have counts for
5 papers
Enabling SQL-based Training Data Debugging for Federated Learning
Yejia Liu, Weiyuan Wu, Lampros Flokas +2
How can we debug a logistical regression model in a federated learning setting when seeing the model behave unexpectedly (e.g., the model rejects all high-income customers' loan ap…
DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python
Jinglin Peng, Weiyuan Wu, Brandon Lockhart +6
Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA ta…
Explaining Inference Queries with Bayesian Optimization
Brandon Lockhart, Jinglin Peng, Weiyuan Wu +2
Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks…
Are We Ready For Learned Cardinality Estimation?
Xiaoying Wang, Changbo Qu, Weiyuan Wu +2
Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned…
Complaint-driven Training Data Debugging for Query 2.0
Weiyuan Wu, Lampros Flokas, Eugene Wu +1
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", whi…