Program Synthesis For Data Science Information Guide

  1. Introduction on Program Synthesis For Data Science
  2. Core Information
  3. History
  4. Detailed Analysis
  5. Conclusion

Introduction on Program Synthesis For Data Science

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Core Information

Program Synthesis via Deep Learning over Graph Structured Data Update
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History

Program Synthesis meets Notebooks Guide
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Program Synthesis for the Masses
Program Synthesis for the Masses
Program Synthesis meets Machine Learning by Sriram Rajamani
Program Synthesis meets Machine Learning by Sriram Rajamani
The Four Big Bets (Illustrated via a Journey in Program Synthesis)
The Four Big Bets (Illustrated via a Journey in Program Synthesis)
Ulrich Schaechtle: Automated data modeling for science via Bayesian synthesis
Ulrich Schaechtle: Automated data modeling for science via Bayesian synthesis
SYNT 2020: Neuro-Symbolic Program Synthesis from Natural Language and Demonstrations
SYNT 2020: Neuro-Symbolic Program Synthesis from Natural Language and Demonstrations
Interpretable Program Synthesis
Interpretable Program Synthesis
Advanced Machine Learning Day 3: Neural Program Synthesis
Advanced Machine Learning Day 3: Neural Program Synthesis
Exploring Program Synthesis: Francois Chollet, Kevin Ellis, Zenna Tavares
Exploring Program Synthesis: Francois Chollet, Kevin Ellis, Zenna Tavares
Towards Human-Like Program Synthesis
Towards Human-Like Program Synthesis
Learning based Program Synthesis, Xinyun Chen, Google Research
Learning based Program Synthesis, Xinyun Chen, Google Research
Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA
Osbert Bastani - Interpretable Machine Learning via Program Synthesis - IPAM at UCLA

Detailed Analysis

Data is compiled from public records and verified media reports.

Last Updated: October 2, 2026

Conclusion

Introduction to Program Synthesis - Lecture 1 News
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Summary

Talk by Rohan Bavishi in the IARCS Verification Seminar Series, on January 4, 2022. More details can be found on the webpage: ... Mayur Naik (University of Pennsylvania) simons.berkeley.edu/talks/tbd-297 The material for this entire video series is based on the official MIT graduate course: 6.5130: Introduction to New computing platforms have greatly increased the demand for programmers, but learning to Sumit Gulwani will talk about the four bets that he made in the second half of his research career, which yielded greater personal ... Panel discussion with Francois Chollet, Kevin Ellis, and Zenna Tavares on why Rishabh Singh (Google) simons.berkeley.edu/talks/tbd-295 ... the 9 billion models in particular if you focus on this Recorded 10 January 2023. Osbert Bastani of the University of Pennsylvania presents "Interpretable Machine Learning via ...

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