Charles Sutton

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 119

Top Venues

Publications

Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans
UQE: A Query Engine for Unstructured Databases. CoRR (2024)
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
A Probabilistic Framework for Modular Continual Learning. ICLR (2024)
Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin
NExT: Teaching Large Language Models to Reason about Code Execution. CoRR (2024)
Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin, Manzil Zaheer, Charles Sutton
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. ICLR (2024)
Matthew Douglas Hoffman, Du Phan, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous
Training Chain-of-Thought via Latent-Variable Inference. NeurIPS (2023)
Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, Denny Zhou
Universal Self-Consistency for Large Language Model Generation. CoRR (2023)
Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. CoRR (2023)
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel
PaLM: Scaling Language Modeling with Pathways. J. Mach. Learn. Res. 24 (2023)
Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. CoRR (2023)
Michael Pradel, Baishakhi Ray, Charles Sutton, Eran Yahav
Programming Language Processing (Dagstuhl Seminar 23062). Dagstuhl Reports 13 (2) (2023)
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton
A Probabilistic Framework for Modular Continual Learning. CoRR (2023)
Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. NeurIPS (2023)
Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai
Any-scale Balanced Samplers for Discrete Space. ICLR (2023)
Du Phan, Matthew D. Hoffman, David Dohan, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous
Training Chain-of-Thought via Latent-Variable Inference. CoRR (2023)
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Oleksandr Polozov, Charles Sutton
Natural Language to Code Generation in Interactive Data Science Notebooks. ACL (1) (2023)
Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin
Can Large Language Models Reason about Program Invariants? ICML (2023)
Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. ICLR (2022)
Simão Eduardo, Kai Xu, Alfredo Nazábal, Charles Sutton
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs. CoRR (2022)
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton, Matthijs Vákár
Conditional Independence by Typing. ACM Trans. Program. Lang. Syst. 44 (1) (2022)
Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. CoRR (2022)
Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton
Natural Language to Code Generation in Interactive Data Science Notebooks. CoRR (2022)
David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Murphy, Charles Sutton
Language Model Cascades. CoRR (2022)
David Bieber, Kensen Shi, Petros Maniatis, Charles Sutton, Vincent J. Hellendoorn, Daniel D. Johnson, Daniel Tarlow
A Library for Representing Python Programs as Graphs for Machine Learning. CoRR (2022)
Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton
Compositional Generalization and Decomposition in Neural Program Synthesis. CoRR (2022)
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel
PaLM: Scaling Language Modeling with Pathways. CoRR (2022)
Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, Richard Ho, Charles Sutton
Learning Semantic Representations to Verify Hardware Designs. NeurIPS (2021)
Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. NeurIPS (2021)
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, Charles Sutton
Program Synthesis with Large Language Models. CoRR (2021)
Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge
Couplings for Multinomial Hamiltonian Monte Carlo. AISTATS (2021)
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou
SpreadsheetCoder: Formula Prediction from Semi-structured Context. CoRR (2021)
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. ICLR (2021)
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, Augustus Odena
Show Your Work: Scratchpads for Intermediate Computation with Language Models. CoRR (2021)
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer
Latent Programmer: Discrete Latent Codes for Program Synthesis. ICML (2021)
Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou
SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML (2021)
Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer
Latent Programmer: Discrete Latent Codes for Program Synthesis. CoRR (2020)
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration. CoRR (2020)
Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR (2020)
Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer, Charles Sutton
Towards Modular Algorithm Induction. CoRR (2020)
Rafael-Michael Karampatsis, Charles Sutton
How Often Do Single-Statement Bugs Occur?: The ManySStuBs4J Dataset. MSR (2020)
Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. AISTATS (2020)
Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton
Neural Program Synthesis with a Differentiable Fixer. CoRR (2020)
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
Open-vocabulary models for source code. ICSE (Companion Volume) (2020)
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber
Global Relational Models of Source Code. ICLR (2020)
Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS (2020)
Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints. CoRR (2020)
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. CoRR (2020)
Annie Louis, Santanu Kumar Dash, Earl T. Barr, Michael D. Ernst, Charles Sutton
Where should I comment my code?: a dataset and model for predicting locations that need comments. ICSE (NIER) (2020)
Augustus Odena, Charles Sutton
Learning to Represent Programs with Property Signatures. CoRR (2020)
Kensen Shi, David Bieber, Charles Sutton
Incremental Sampling Without Replacement for Sequence Models. CoRR (2020)
Kensen Shi, David Bieber, Charles Sutton
Incremental Sampling Without Replacement for Sequence Models. ICML (2020)
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code. CoRR (2020)
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes
Big code != big vocabulary: open-vocabulary models for source code. ICSE (2020)
David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. NeurIPS (2020)
Augustus Odena, Charles Sutton
Learning to Represent Programs with Property Signatures. ICLR (2020)
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
Learning to Fix Build Errors with Graph2Diff Neural Networks. ICSE (Workshops) (2020)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Generative Ratio Matching Networks. ICLR (2020)
Rafael-Michael Karampatsis, Charles Sutton
SCELMo: Source Code Embeddings from Language Models. CoRR (2020)
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton, Matthijs Vákár
Conditional independence by typing. CoRR (2020)
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic. Proc. ACM Program. Lang. 3 (POPL) (2019)
Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data. CoRR (2019)
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian
Learning to Fix Build Errors with Graph2Diff Neural Networks. CoRR (2019)
Rafael-Michael Karampatsis, Charles Sutton
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code. CoRR (2019)
Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton
Learning Semantic Annotations for Tabular Data. CoRR (2019)
Rafael-Michael Karampatsis, Charles Sutton
How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset. CoRR (2019)
Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton
GEMSEC: graph embedding with self clustering. ASONAM (2019)
Gerrit J. J. van den Burg, Alfredo Nazábal, Charles Sutton
Wrangling messy CSV files by detecting row and type patterns. Data Min. Knowl. Discov. 33 (6) (2019)
Kai Xu, Akash Srivastava, Charles Sutton
Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML (2019)
Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton
Learning Semantic Annotations for Tabular Data. IJCAI (2019)
Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. AAAI (2019)
Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton
GEMSEC: Graph Embedding with Self Clustering. CoRR (2018)
Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic. CoRR (2018)
Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar T. Devanbu, Mark Marron, Charles Sutton
Mining Semantic Loop Idioms. IEEE Trans. Software Eng. 44 (7) (2018)
Tomas Petricek, James Geddes, Charles Sutton
Wrattler: Reproducible, live and polyglot notebooks. TaPP (2018)
Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana
Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD (2018)
Nikolaos Katirtzis, Themistoklis Diamantopoulos, Charles Sutton
Summarizing Software API Usage Examples Using Clustering Techniques. FASE (2018)
Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel H. Goddard, Charles Sutton
Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring. AAAI (2018)
Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge. CoRR (2018)
Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. CoRR (2018)
Gerrit J. J. van den Burg, Alfredo Nazábal, Charles Sutton
Wrangling Messy CSV Files by Detecting Row and Type Patterns. CoRR (2018)
Annie Louis, Charles Sutton
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts. NAACL-HLT (2) (2018)
Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
Deep Learning to Detect Redundant Method Comments. CoRR (2018)
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
HOUDINI: Lifelong Learning as Program Synthesis. NeurIPS (2018)
Akash Srivastava, Charles Sutton
Variational Inference In Pachinko Allocation Machines. CoRR (2018)
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
Synthesis of Differentiable Functional Programs for Lifelong Learning. CoRR (2018)
Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, Charles Sutton
A Survey of Machine Learning for Big Code and Naturalness. ACM Comput. Surv. 51 (4) (2018)
Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR (2018)
Charles Sutton, Linan Gong
Popularity of arXiv.org within Computer Science. CoRR (2017)
Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, Charles Sutton
A Survey of Machine Learning for Big Code and Naturalness. CoRR (2017)
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton
Learning Continuous Semantic Representations of Symbolic Expressions. ICML (2017)
Akash Srivastava, Charles Sutton
Autoencoding Variational Inference For Topic Models. ICLR (Poster) (2017)
Jaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton
Autofolding for Source Code Summarization. IEEE Trans. Software Eng. 43 (12) (2017)
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS (2017)
Jaroslav M. Fowkes, Charles Sutton
A Subsequence Interleaving Model for Sequential Pattern Mining. KDD (2016)
Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR (2016)
Jaroslav M. Fowkes, Charles Sutton
A Subsequence Interleaving Model for Sequential Pattern Mining. CoRR (2016)
Jaroslav M. Fowkes, Charles Sutton
A Bayesian Network Model for Interesting Itemsets. ECML/PKDD (2) (2016)
Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik
A text-mining approach to explain unwanted behaviours. EUROSEC (2016)
Krzysztof J. Geras, Charles Sutton
Composite Denoising Autoencoders. ECML/PKDD (1) (2016)
Daniel Duma, Charles Sutton, Ewan Klein
Context Matters: Towards Extracting a Citation's Context Using Linguistic Features. JCDL (2016)
Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton
Learning Continuous Semantic Representations of Symbolic Expressions. CoRR (2016)

Charles Sutton.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Charles Sutton