| 2024 | ICLR | ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. | Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin, Manzil Zaheer, Charles Sutton |
| 2024 | ICLR | A Probabilistic Framework for Modular Continual Learning. | Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton |
| 2024 | ICML | NExT: Teaching Large Language Models to Reason about Code Execution. | Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin |
| 2023 | ACL | Natural Language to Code Generation in Interactive Data Science Notebooks. | 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 |
| 2023 | ICLR | Any-scale Balanced Samplers for Discrete Space. | Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai |
| 2023 | ICML | Can Large Language Models Reason about Program Invariants? | Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin |
| 2022 | ICLR | CrossBeam: Learning to Search in Bottom-Up Program Synthesis. | Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton |
| 2021 | AISTATS | Couplings for Multinomial Hamiltonian Monte Carlo. | Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge |
| 2021 | ICLR | BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. | Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai |
| 2021 | ICML | SpreadsheetCoder: Formula Prediction from Semi-structured Context. | Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou |
| 2021 | ICML | Latent Programmer: Discrete Latent Codes for Program Synthesis. | Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer |
| 2020 | AISTATS | Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. | Simo Eduardo, Alfredo Nazbal, Christopher K. I. Williams, Charles Sutton |
| 2020 | ICLR | Global Relational Models of Source Code. | Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber |
| 2020 | ICLR | Learning to Represent Programs with Property Signatures. | Augustus Odena, Charles Sutton |
| 2020 | ICLR | Generative Ratio Matching Networks. | Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton |
| 2020 | ICML | Incremental Sampling Without Replacement for Sequence Models. | Kensen Shi, David Bieber, Charles Sutton |
| 2020 | ICSE | Big code != big vocabulary: open-vocabulary models for source code. | Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes |
| 2020 | ICSE | Open-vocabulary models for source code. | Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes |
| 2020 | ICSE | Where should I comment my code?: a dataset and model for predicting locations that need comments. | Annie Louis, Santanu Kumar Dash, Earl T. Barr, Michael D. Ernst, Charles Sutton |
| 2020 | ICSE | Learning to Fix Build Errors with Graph2Diff Neural Networks. | Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, Edward Aftandilian |
| 2020 | MSR | How Often Do Single-Statement Bugs Occur?: The ManySStuBs4J Dataset. | Rafael-Michael Karampatsis, Charles Sutton |
| 2019 | AAAI | ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. | Jiaoyan Chen, Ernesto Jimnez-Ruiz, Ian Horrocks, Charles Sutton |
| 2019 | ICML | Variational Russian Roulette for Deep Bayesian Nonparametrics. | Kai Xu, Akash Srivastava, Charles Sutton |
| 2019 | IJCAI | Learning Semantic Annotations for Tabular Data. | Jiaoyan Chen, Ernesto Jimnez-Ruiz, Ian Horrocks, Charles Sutton |
| 2018 | AAAI | Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring. | Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel H. Goddard, Charles Sutton |
| 2018 | FASE | Summarizing Software API Usage Examples Using Clustering Techniques. | Nikolaos Katirtzis, Themistoklis Diamantopoulos, Charles Sutton |
| 2018 | KDD | Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. | Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana |
| 2018 | NAACL | Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts. | Annie Louis, Charles Sutton |
| 2017 | ICLR | Autoencoding Variational Inference For Topic Models. | Akash Srivastava, Charles Sutton |
| 2017 | ICML | Learning Continuous Semantic Representations of Symbolic Expressions. | Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton |
| 2016 | ICML | A Convolutional Attention Network for Extreme Summarization of Source Code. | Miltiadis Allamanis, Hao Peng, Charles Sutton |
| 2016 | ICSE | TASSAL: autofolding for source code summarization. | Jaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton |
| 2016 | IFM | On Robust Malware Classifiers by Verifying Unwanted Behaviours. | Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik |
| 2016 | KDD | A Subsequence Interleaving Model for Sequential Pattern Mining. | Jaroslav M. Fowkes, Charles Sutton |
| 2014 | WWW | Word storms: multiples of word clouds for visual comparison of documents. | Quim Castell, Charles Sutton |
| 2013 | ICML | Multiple-source cross-validation. | Krzysztof J. Geras, Charles Sutton |
| 2013 | MSR | Why, when, and what: analyzing stack overflow questions by topic, type, and code. | Miltiadis Allamanis, Charles Sutton |
| 2013 | MSR | Mining source code repositories at massive scale using language modeling. | Miltiadis Allamanis, Charles Sutton |
| 2009 | CIDR | Capturing Data Uncertainty in High-Volume Stream Processing. | Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Charles Sutton, Thanh T. L. Tran, Michael Zink |
| 2009 | ICDE | Probabilistic Inference over RFID Streams in Mobile Environments. | Thanh T. L. Tran, Charles Sutton, Richard Cocci, Yanming Nie, Yanlei Diao, Prashant J. Shenoy |
| 2008 | KDD | Unsupervised deduplication using cross-field dependencies. | Robert J. Hall, Charles Sutton, Andrew McCallum |
| 2008 | NSDI | Exploiting Machine Learning to Subvert Your Spam Filter. | Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. Doug Tygar, Kai Xia |
| 2008 | OSDI | Probabilistic Inference in Queueing Networks. | Charles Sutton, Michael I. Jordan |
| 2007 | ICML | Piecewise pseudolikelihood for efficient training of conditional random fields. | Charles Sutton, Andrew McCallum |
| 2007 | UAI | Improved Dynamic Schedules for Belief Propagation. | Charles Sutton, Andrew McCallum |
| 2006 | ICASSP | Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields. | Chris Pal, Charles Sutton, Andrew McCallum |
| 2006 | NAACL | Reducing Weight Undertraining in Structured Discriminative Learning. | Charles Sutton, Michael Sindelar, Andrew McCallum |
| 2005 | AISTATS | Learning in Markov Random Fields with Contrastive Free Energies. | Max Welling, Charles Sutton |
| 2005 | CoNLL | Joint Parsing and Semantic Role Labeling. | Charles Sutton, Andrew McCallum |
| 2005 | NAACL | Composition of Conditional Random Fields for Transfer Learning. | Charles Sutton, Andrew McCallum |
| 2005 | UAI | Piecewise Training for Undirected Models. | Charles Sutton, Andrew McCallum |
| 2004 | ICML | Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. | Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum |