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Wed 11 Nov 2020 17:30 - 17:32 at Virtual room 2 - ML Model Building

We consider a usage model for automated machine learning (AutoML) in which users
can influence the generated pipeline by providing a weak pipeline
specification: an unordered set of API components from which the AutoML
system draws the components it places into the generated pipeline.
Such specifications allow users to express preferences over the components that appear in the
pipeline, for example a desire for interpretable components to appear
in the pipeline. We present AMS, an approach to automatically strengthen
weak specifications to include unspecified
complementary and functionally related API components, populate the space of
hyperparameters and their values, and pair this configuration with a search
procedure to produce a strong pipeline specification: a full
description of the search space for candidate pipelines. ams uses
normalized pointwise mutual information on a code corpus to identify
complementary components, BM25 as a lexical similarity score
over the target API's documentation to identify
functionally related components, and frequency distributions in the code corpus to
extract key hyperparameters and values. We show that strengthened specifications
can produce pipelines that outperform the pipelines generated from the
initial weak specification and an expert-annotated variant, while producing pipelines that still
reflect the user preferences captured in the original weak specification.

Wed 11 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change

17:30 - 17:32
Talk
AMS: Generating AutoML Search Spaces from Weak Specifications
Research Papers
Jose CambroneroMassachusetts Institute of Technology, USA, Jürgen CitoTU Wien and MIT, Martin RinardMassachusetts Institute of Technology, USA
DOI
17:33 - 17:34
Talk
Continuous Experimentation on Artificial Intelligence Software: A Research Agenda
Visions and Reflections
Anh Nguyen-DucUniversity of South Eastern Norway, Pekka AbrahamssonUniversity of Jyväskylä
DOI
17:35 - 17:36
Talk
DENAS: Automated Rule Generation by Knowledge Extraction from Neural Networks
Research Papers
SiminChen University of Texas at Dallas, USA, Soroush BateniUniversity of Texas at Dallas, USA, Sampath GrandhiUniversity of Texas at Dallas, USA, Xiaodi LiUniversity of Texas at Dallas, USA, Cong LiuUniversity of Texas at Dallas, USA, Wei YangUniversity of Texas at Dallas, USA
DOI
17:37 - 17:38
Talk
On Decomposing a Deep Neural Network into ModulesACM SIGSOFT Distinguished Paper Award
Research Papers
Rangeet PanIowa State University, USA, Hridesh RajanIowa State University, USA
DOI Media Attached
17:39 - 17:40
Talk
Synthesizing Correct Code for Machine Learning Programs
Student Research Competition
Joshua GisiNorth Dakota State University, USA
DOI
17:41 - 18:00
Talk
Conversations on ML Model Building
Paper Presentations
Jose CambroneroMassachusetts Institute of Technology, USA, Rangeet PanIowa State University, USA, Simin Chen, Wei YangUniversity of Texas at Dallas, USA, M: John-Paul OreNorth Carolina State University