22 citations · 44 across the 6 of their papers we have counts for
6 papers
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Peng Li, Zhiyi Chen, Xu Chu +1
Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models. However, it can be costly and time…
Ground Truth Inference for Weakly Supervised Entity Matching
Renzhi Wu, Alexander Bendeck, Xu Chu +1
Entity matching (EM) refers to the problem of identifying pairs of data records in one or more relational tables that refer to the same entity in the real world. Supervised machine…
iFlipper: Label Flipping for Individual Fairness
Hantian Zhang, Ki Hyun Tae, Jaeyoung Park +2
As machine learning becomes prevalent, mitigating any unfairness present in the training data becomes critical. Among the various notions of fairness, this paper focuses on the wel…
Learning Hyper Label Model for Programmatic Weak Supervision
Renzhi Wu, Shen-En Chen, Jieyu Zhang +1
To reduce the human annotation efforts, the programmatic weak supervision (PWS) paradigm abstracts weak supervision sources as labeling functions (LFs) and involves a label model t…
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Renzhi Wu, Bolin Ding, Xu Chu +4
Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus…
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
Hantian Zhang, Xu Chu, Abolfazl Asudeh +1
Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or femal…