11 citations · 46 across the 22 of their papers we have counts for
26 papers
Efficient Query Repair for Aggregate Constraints
Shatha Algarni, Boris Glavic, Seokki Lee +1
In many real-world scenarios, query results must satisfy domain-specific constraints. For instance, a minimum percentage of interview candidates selected based on their qualificati…
Refining Labeling Functions with Limited Labeled Data
Chenjie Li, Amir Gilad, Boris Glavic +2
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints.…
Stress-Testing ML Pipelines with Adversarial Data Corruption
Jiongli Zhu, Geyang Xu, Felipe Lorenzi +2
Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of ma…
In-memory Incremental Maintenance of Provenance Sketches [extended version]
Pengyuan Li, Boris Glavic, Dieter Gawlick +4
Provenance-based data skipping compactly over-approximates the provenance of a query using so-called provenance sketches and utilizes such sketches to speed-up the execution of sub…
Cost-based Selection of Provenance Sketches for Data Skipping
Ziyu Liu, Boris Glavic
Provenance sketches, light-weight indexes that record what data is needed (is relevant) for answering a query, can significantly improve performance of important classes of queries…
Learning from Uncertain Data: From Possible Worlds to Possible Models
Jiongli Zhu, Su Feng, Boris Glavic +1
We introduce an efficient method for learning linear models from uncertain data, where uncertainty is represented as a set of possible variations in the data, leading to predictive…