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3:30 p.m.
Online presentation
For More Information:
Soheil Feizi
sfeizi@umd.edu
https://umd.zoom.us/j/95197245230?pwd=cDRlVWRVeXBHcURGQkptSHpIS0VGdz09
Computer Science Department Machine Learning Seminar
Learning-Based Program Synthesis
Xinyun Chen
Senior Research Scientist, "Brain Team"
Google Research
Zoom link
https://umd.zoom.us/j/95197245230?pwd=cDRlVWRVeXBHcURGQkptSHpIS0VGdz09
Password 828w
Abstract
With the advancement of modern technologies, programming becomes ubiquitous not only among professional software developers, but also for general computer users. However, gaining programming expertise is time-consuming and challenging. Therefore, program synthesis has many applications, where the computer automatically synthesizes programs from specifications such as natural language descriptions and input-output examples. In this talk, I will present my work on learning-based program synthesis, where we have developed deep learning techniques to handle 3 core challenges of program synthesis: input ambiguity, program complexity, and generalization. The talk will have 3 parts : (1) learning to synthesize programs from multi-modal and potentially ambiguous specifications in the wild; (2) learning with execution for solving challenging programming problems; and (3) compositional generalization via program learning.
Biography
Xinyun Chen is a senior research scientist in the Brain team of Google Research. She obtained her Ph.D. in Computer Science from University of California, Berkeley. Her research lies at the intersection of deep learning, programming languages, and security. Her recent research focuses on learning-based program synthesis and adversarial machine learning. She received the Facebook Fellowship in 2020, and Rising Stars in Machine Learning in 2021. Her work SpreadsheetCoder for spreadsheet formula prediction was integrated into Google Sheets, and she was part of the AlphaCode team when she interned at DeepMind.
Readings
1. SpreadsheetCoder: Formula Prediction from Semi-structured Context
2. Execution-Guided Neural Program Synthesis
3. Compositional Generalization via Neural-Symbolic Stack Machines
4. (additional) Competition-Level Code Generation with AlphaCode
5. (additional) Compositional Semantic Parsing with Large Language Models
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Event Information
