We present a new approach for learning programs from noisy datasets. Our approach is based on two new concepts: a regularized program generator which predicts a candidate program based on a small sample of the entire dataset while avoiding overfitting, and a dataset sampler which carefully samples the dataset by leveraging the candidate program’s score on that dataset. The two components are connected in a continuous feedback-directed loop.
We show how to instantiate our learning approach to three settings: the setting where the dataset contains no noise (as assumed in today’s programming-by-example approaches), the setting where the dataset has a bound on the noise, and the setting where no bound on the noise is known. We then present several new kinds of program synthesizers which target a particular noise setting.
First, we introduce a novel regularized bitstream synthesizer that successfully predicts programs even in the presence of incorrect input/output examples. We show that the synthesizer can detect errors in the examples while combatting overfitting – a major problem in existing synthesis techniques. We also show how the approach can be used in a setting where the dataset grows dynamically via new examples (e.g., provided by a human).
Second, we present a novel technique for constructing statistical code completion systems. These are systems trained on massive datasets of open source programs, also known as “Big Code”. The key idea is to introduce a domain specific language (DSL) over trees and to learn functions in that DSL directly from the dataset. These learned functions then condition the predictions made by the system. This is a flexible and powerful technique which generalizes several existing works as we no longer need to decide a priori on what the prediction should be conditioned (another benefit is that the learned functions act as a natural mechanism for explaining the prediction to the programmer). As a result, our code completion system surpasses the prediction capabilities of state of the art, hard-wired systems.
Fri 22 JanDisplayed time zone: Guadalajara, Mexico City, Monterrey change
14:20 - 16:00
|Learning Programs from Noisy Data
Veselin Raychev ETH Zurich, Pavol Bielik ETH Zurich, Switzerland, Martin Vechev ETH Zurich, Andreas Krause ETH ZurichLink to publication DOI Pre-print Media Attached File Attached
|Optimizing Synthesis with Metasketches
James Bornholt University of Washington, Emina Torlak University of Washington, Dan Grossman University of Washington, USA, Luis Ceze University of Washington, USAPre-print Media Attached
|Maximal Specification Synthesis
Aws Albarghouthi University of Wisconsin–Madison, Işıl Dillig University of Texas, Austin, Arie Gurfinkel Carnegie Mellon UniversityPre-print Media Attached
|Example-Directed Synthesis: A Type-Theoretic Interpretation
Jonathan Frankle Princeton University, Peter-Michael Osera Grinnell College, David Walker Princeton University, Steve Zdancewic University of PennsylvaniaPre-print Media Attached File Attached