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Author Information
Augustus Odena (Google Brain)
Maxwell Nye (MIT)
Disha Shrivastava (Mila, University of Montreal)
Mayank Agarwal (IBM Research AI, MIT-IBM Watson AI Lab)
Vincent J Hellendoorn (CMU)
I create intelligent tools for software engineers using machine learning. The potential of this intersection is tremendous: artificially intelligent models can (re)learn many software development processes and provide valuable support in coding, debugging, optimization, ensuring security, and more. But programming is also a very human activity, so supporting developers effectively is non-trivial: many of the most interesting tasks require rich insights into how developers write and reason about software, and my research has shown how popular models without those insights are often mismatched to practice. Instead, I simultaneously study machine learning and software engineering research. My work makes both fundamental advances in deep learning models for source code, leverages empirical methods to enable ground-breaking new tasks, and reflects on current models with real developer data to ensure that we are moving in the right direction.
Charles Sutton (Google)
More from the Same Authors
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2021 Spotlight: PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair »
Zimin Chen · Vincent J Hellendoorn · Pascal Lamblin · Petros Maniatis · Pierre-Antoine Manzagol · Daniel Tarlow · Subhodeep Moitra -
2021 : Type Inference as Optimization »
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2021 : COVID-19 India Dataset: Parsing Detailed COVID-19 Data in Daily Health Bulletins from States in India »
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2022 Poster: Communicating Natural Programs to Humans and Machines »
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2021 Workshop: Advances in Programming Languages and Neurosymbolic Systems (AIPLANS) »
Breandan Considine · Disha Shrivastava · David Yu-Tung Hui · Chin-Wei Huang · Shawn Tan · Xujie Si · Prakash Panangaden · Guy Van den Broeck · Daniel Tarlow -
2021 Poster: Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning »
Maxwell Nye · Michael Tessler · Josh Tenenbaum · Brenden Lake -
2021 Poster: Learning Semantic Representations to Verify Hardware Designs »
Shobha Vasudevan · Wenjie (Joe) Jiang · David Bieber · Rishabh Singh · hamid shojaei · C. Richard Ho · Charles Sutton -
2021 Poster: Learning to Combine Per-Example Solutions for Neural Program Synthesis »
Disha Shrivastava · Hugo Larochelle · Daniel Tarlow -
2021 Poster: A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics »
Kai Xu · Akash Srivastava · Dan Gutfreund · Felix Sosa · Tomer Ullman · Josh Tenenbaum · Charles Sutton -
2021 Poster: PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair »
Zimin Chen · Vincent J Hellendoorn · Pascal Lamblin · Petros Maniatis · Pierre-Antoine Manzagol · Daniel Tarlow · Subhodeep Moitra -
2021 Poster: On sensitivity of meta-learning to support data »
Mayank Agarwal · Mikhail Yurochkin · Yuekai Sun -
2020 : closing talk »
Augustus Odena · Charles Sutton -
2020 : Panel »
Augustus Odena · Charles Sutton · Roopsha Samanta · Xinyun Chen · Elena Glassman -
2020 : Satish Chandra Talk »
Satish Chandra · Augustus Odena · Charles Sutton -
2020 : Spotlight Session 2 »
Augustus Odena · Kensen Shi · David Bieber · Ferran Alet · Charles Sutton · Roshni Iyer -
2020 : NLC2CMD Competition Organizers: Metrics, Data, Tracks »
Mayank Agarwal -
2020 : NLC2CMD Competition Keynote: Tellina »
Victoria Lin · Mayank Agarwal · Tathagata Chakraborti -
2020 : NLC2CMD Competition Organizers: Introduction, Problem Description, CLAI »
Mayank Agarwal -
2020 Workshop: Workshop on Computer Assisted Programming (CAP) »
Augustus Odena · Charles Sutton · Nadia Polikarpova · Josh Tenenbaum · Armando Solar-Lezama · Isil Dillig -
2020 : Welcome Talk »
Augustus Odena -
2020 Poster: Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples »
Samarth Sinha · Zhengli Zhao · Anirudh Goyal · Colin A Raffel · Augustus Odena -
2020 Poster: Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks »
David Bieber · Charles Sutton · Hugo Larochelle · Danny Tarlow -
2020 Poster: Learning Compositional Rules via Neural Program Synthesis »
Maxwell Nye · Armando Solar-Lezama · Josh Tenenbaum · Brenden Lake -
2020 Poster: SMYRF - Efficient Attention using Asymmetric Clustering »
Giannis Daras · Nikita Kitaev · Augustus Odena · Alex Dimakis -
2020 Poster: Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration »
Hanjun Dai · Rishabh Singh · Bo Dai · Charles Sutton · Dale Schuurmans -
2019 : Poster Session + Lunch »
Maxwell Nye · Robert Kim · Toby St Clere Smithe · Takeshi D. Itoh · Omar U. Florez · Vesna G. Djokic · Sneha Aenugu · Mariya Toneva · Imanol Schlag · Dan Schwartz · Max Raphael Sobroza Marques · Pravish Sainath · Peng-Hsuan Li · Rishi Bommasani · Najoung Kim · Paul Soulos · Steven Frankland · Nadezhda Chirkova · Dongqi Han · Adam Kortylewski · Rich Pang · Milena Rabovsky · Jonathan Mamou · Vaibhav Kumar · Tales Marra -
2019 : Learning Compositional Rules via Neural Program Synthesis »
Maxwell Nye -
2019 Poster: Write, Execute, Assess: Program Synthesis with a REPL »
Kevin Ellis · Maxwell Nye · Yewen Pu · Felix Sosa · Josh Tenenbaum · Armando Solar-Lezama -
2019 Demonstration: Project BB: Bringing AI to the Command Line »
Tathagata Chakraborti · Mayank Agarwal -
2019 Poster: Statistical Model Aggregation via Parameter Matching »
Mikhail Yurochkin · Mayank Agarwal · Soumya Ghosh · Kristjan Greenewald · Nghia Hoang -
2018 : Panel on research process »
Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth -
2018 : Charles Sutton »
Charles Sutton -
2018 Poster: Realistic Evaluation of Deep Semi-Supervised Learning Algorithms »
Avital Oliver · Augustus Odena · Colin A Raffel · Ekin Dogus Cubuk · Ian Goodfellow -
2018 Spotlight: Realistic Evaluation of Deep Semi-Supervised Learning Algorithms »
Avital Oliver · Augustus Odena · Colin A Raffel · Ekin Dogus Cubuk · Ian Goodfellow -
2018 Poster: HOUDINI: Lifelong Learning as Program Synthesis »
Lazar Valkov · Dipak Chaudhari · Akash Srivastava · Charles Sutton · Swarat Chaudhuri -
2017 Poster: VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning »
Akash Srivastava · Lazar Valkov · Chris Russell · Michael Gutmann · Charles Sutton -
2016 Workshop: Towards an Artificial Intelligence for Data Science »
Charles Sutton · James Geddes · Zoubin Ghahramani · Padhraic Smyth · Chris Williams -
2015 Poster: Latent Bayesian melding for integrating individual and population models »
Mingjun Zhong · Nigel Goddard · Charles Sutton -
2015 Spotlight: Latent Bayesian melding for integrating individual and population models »
Mingjun Zhong · Nigel Goddard · Charles Sutton -
2014 Poster: Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models »
Yichuan Zhang · Charles Sutton -
2014 Poster: Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation »
Mingjun Zhong · Nigel Goddard · Charles Sutton -
2012 Poster: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2012 Spotlight: Continuous Relaxations for Discrete Hamiltonian Monte Carlo »
Zoubin Ghahramani · Yichuan Zhang · Charles Sutton · Amos Storkey -
2011 Poster: Quasi-Newton Methods for Markov Chain Monte Carlo »
Yichuan Zhang · Charles Sutton