AI for Low-Code for AI

no code implementations • 31 May 2023 • Nikitha Rao, Jason Tsay, Kiran Kate, Vincent J. Hellendoorn, Martin Hirzel

We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks.

Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)

no code implementations • 22 Feb 2022 • Jason Tsay, Andrea Bartezzaghi, Aleke Nolte, Cristiano Malossi

We present the Lifelong Database of Experiments (LDE) that automatically extracts and stores linked metadata from experiment artifacts and provides features to reproduce these artifacts and perform meta-learning across them.

Meta-Learning

Pipeline Combinators for Gradual AutoML

no code implementations • NeurIPS 2021 • Guillaume Baudart, Martin Hirzel, Kiran Kate, Parikshit Ram, Avraham Shinnar, Jason Tsay

Automated machine learning (AutoML) can make data scientists more productive.

AutoML BIG-bench Machine Learning +1

FactSheets: Increasing Trust in AI Services through Supplier's Declarations of Conformity

no code implementations • 22 Aug 2018 • Matthew Arnold, Rachel K. E. Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilovic, Ravi Nair, Karthikeyan Natesan Ramamurthy, Darrell Reimer, Alexandra Olteanu, David Piorkowski, Jason Tsay, Kush R. Varshney

We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers.

Fairness