12 citations · 20 across the 5 of their papers we have counts for
6 papers
ARDA: Automatic Relational Data Augmentation for Machine Learning
Nadiia Chepurko, Ryan Marcus, Emanuel Zgraggen +3
Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more…
Sherlock: A Deep Learning Approach to Semantic Data Type Detection
Madelon Hulsebos, Kevin Hu, Michiel Bakker +5
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparat…
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
Kevin Hu, Neil Gaikwad, Michiel Bakker +7
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the charact…
STAR: Statistical Tests with Auditable Results
Sacha Servan-Schreiber, Olga Ohrimenko, Tim Kraska +1
We present STAR: a novel system aimed at solving the complex issue of "p-hacking" and false discoveries in scientific studies. STAR provides a concrete way for ensuring the applica…
IDEBench: A Benchmark for Interactive Data Exploration
Philipp Eichmann, Carsten Binnig, Tim Kraska +1
Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios. The main metric of these benchmarks is the performance of…
Controlling False Discoveries During Interactive Data Exploration
Zheguang Zhao, Lorenzo De Stefani, Emanuel Zgraggen +3
Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. The crux is that these tools implicitly allow the user to test a…