\dm_csml_event_details UCL ELLIS

Learning to Code: Machine Learning for Program Induction


Daniel Tarlow


Microsoft Research Cambridge


Friday, 11 November 2016






Roberts Building 508

Event series

DeepMind/ELLIS CSML Seminar Series


I'll present two of our recent works on using machine learning to induce computer programs from input-output examples. The first system is TerpreT, which enables comparison of machine learning-based program synthesis techniques to programming languages (PL)-based techniques. Based on our learnings from TerpreT, we develop the second system, DeepCoder, which induces programs from input-output examples using a neural network to guide PL-based search techniques. DeepCoder achieves an order of magnitude speedup over optimized search-based techniques, and it can solve problems of difficulty comparable to the simplest problems on programming competition websites.

Bio: Danny Tarlow is a Researcher in the Machine Intelligence and Perception group at Microsoft Research in Cambridge, UK. His research interests are in the application of machine learning to problems involving highly structured data, with a specific interest in the intersection of machine learning and programming languages. He is an editor of the forthcoming MIT Press book on Perturbations, Optimization, and Statistics, and his work has won paper awards at UAI (Best Student Paper, Runner Up), the ICML Workshop on Constructive Machine Learning (Best Paper), and NIPS (Best Paper). He holds a Ph.D. from the Machine Learning group at the University of Toronto (2013) and was previously a Research Fellow at Darwin College, University of Cambridge (2013-2016).