Sutton, Charles

46 publications

ICLR 2024 A Probabilistic Framework for Modular Continual Learning Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton

ICLR 2024 ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin, Manzil Zaheer, Charles Sutton

ICML 2024 NExT: Teaching Large Language Models to Reason About Code Execution Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin

NeurIPS 2024 UQE: A Query Engine for Unstructured Databases Hanjun Dai, Bethany Yixin Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans

ICMLW 2024 Universal Self-Consistency for Large Language Models Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, Denny Zhou

ICLR 2023 Any-Scale Balanced Samplers for Discrete Space Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai

ICML 2023 Can Large Language Models Reason About Program Invariants? Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin

JMLR 2023 PaLM: Scaling Language Modeling with Pathways 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

ICLRW 2022 Compositional Generalization and Decomposition in Neural Program Synthesis Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton

ICLR 2022 CrossBeam: Learning to Search in Bottom-up Program Synthesis Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton

ICLRW 2022 Show Your Work: Scratchpads for Intermediate Computation with Language Models 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

AISTATS 2021 Couplings for Multinomial Hamiltonian Monte Carlo Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge

ICLR 2021 BUSTLE: Bottom-up Program Synthesis Through Learning-Guided Exploration Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai

ICML 2021 Latent Programmer: Discrete Latent Codes for Program Synthesis Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer

ICML 2021 SpreadsheetCoder: Formula Prediction from Semi-Structured Context Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou

NeurIPSW 2021 Type Inference as Optimization Eirene V. Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton

ICLR 2020 Generative Ratio Matching Networks Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

ICLR 2020 Global Relational Models of Source Code Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber

ICML 2020 Incremental Sampling Without Replacement for Sequence Models Kensen Shi, David Bieber, Charles Sutton

ICLR 2020 Learning to Represent Programs with Property Signatures Augustus Odena, Charles Sutton

AISTATS 2020 Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data Simao Eduardo, Alfredo Nazabal, Christopher K. I. Williams, Charles Sutton

AAAI 2019 ColNet: Embedding the Semantics of Web Tables for Column Type Prediction Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton

IJCAI 2019 Learning Semantic Annotations for Tabular Data Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton

ICML 2019 Variational Russian Roulette for Deep Bayesian Nonparametrics Kai Xu, Akash Srivastava, Charles Sutton

NeurIPS 2018 HOUDINI: Lifelong Learning as Program Synthesis Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

AAAI 2018 Sequence-to-Point Learning with Neural Networks for Non-Intrusive Load Monitoring Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel H. Goddard, Charles Sutton

ICLR 2017 Autoencoding Variational Inference for Topic Models Akash Srivastava, Charles Sutton

ICML 2017 Learning Continuous Semantic Representations of Symbolic Expressions Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton

NeurIPS 2017 VEEGAN: Reducing Mode Collapse in GANs Using Implicit Variational Learning Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

ECML-PKDD 2016 A Bayesian Network Model for Interesting Itemsets Jaroslav M. Fowkes, Charles Sutton

ICML 2016 A Convolutional Attention Network for Extreme Summarization of Source Code Miltiadis Allamanis, Hao Peng, Charles Sutton

ECML-PKDD 2016 Composite Denoising Autoencoders Krzysztof J. Geras, Charles Sutton

NeurIPS 2015 Latent Bayesian Melding for Integrating Individual and Population Models Mingjun Zhong, Nigel Goddard, Charles Sutton

ICLR 2015 Scheduled Denoising Autoencoders Krzysztof J. Geras, Charles Sutton

NeurIPS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton

NeurIPS 2014 Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton

ICML 2013 Multiple-Source Cross-Validation Krzysztof Geras, Charles Sutton

FnTML 2012 An Introduction to Conditional Random Fields Charles Sutton, Andrew McCallum

AISTATS 2010 Inference and Learning in Networks of Queues Charles Sutton, Michael I. Jordan

MLJ 2009 Piecewise Training for Structured Prediction Charles Sutton, Andrew McCallum

JMLR 2007 Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh

UAI 2007 Improved Dynamic Schedules for Belief Propagation Charles Sutton, Andrew McCallum

ICML 2007 Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields Charles Sutton, Andrew McCallum

AISTATS 2005 Learning in Markov Random Fields with Contrastive Free Energies Max Welling, Charles Sutton

UAI 2005 Piecewise Training for Undirected Models Charles Sutton, Andrew McCallum

ICML 2004 Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum