Publications

José Cambronero, Jürgen Cito, Micah J. Smith, Martin Rinard (2021). Mining Nearby Repairs that Improve Machine Learning Pipeline Performance. Under submission.

José Cambronero, Raul Casto Fernandez, Martin Rinard (2021). wranglesearch: Mining Data Wrangling Functions from Python Programs. Under submission.

Fatjon Zogaj (equal contrib.), José Cambronero (equal contrib), Martin Rinard, Jürgen Cito (2020). Doing More with Less: Characterizing Dataset Downsampling for AutoML. VLDB 2021.

Limor Appelbaum (equal contrib.), José Cambronero (equal contrib.), Jennifer P. Stevens, Steven Horng, Karla Pollick, George Silva, Sebastien Haneuse, Gail Piatkowski, Nordine Benhaga, Stacey Duey, Mary A. Stevenson, Harvey Mamon, Irving D. Kaplan, Martin Rinard (2020). Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study. European Journal of Cancer.

José Cambronero, Jürgen Cito, Martin Rinard (2020). AMS: Generating AutoML search spaces from weak specifications. FSE 2020.

PDF Project Slides Video

José Cambronero, Martin Rinard (2019). AL: Autogenerating Supervised Learning Programs. SPLASH OOPSLA 2019.

PDF Project Slides

José Cambronero, Thurston H.Y. Dang, Nikos Vasilakis, Jiasi Shen, Jerry Wu, Martin Rinard (2019). Active Learning for Software Engineering. SPLASH Onward! 2019.

José Cambronero (1), Hongyu Li (1), Seohyun Kim, Koushik Sen, Satish Chandra, (1: contributed equally) (2019). When Deep Learning Met Code Search. FSE 2019 (Industry Track).

José Cambronero (1), Jiasi Shen (1), Jürgen Cito (1), Elena Glassman, Martin Rinard, (1: contributed equally) (2019). Characterizing Developer Use of Automatically Generated Patches. VL/HCC 2019 (Short Paper).

José Cambronero (1), John F. Feser (1), Micah J. Smith (1), Samuel Madden, (1: contributed equally) (2017). Query Optimization for Dynamic Imputation. In VLDB 2017.