Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDCharles Sutton
Charles A. Sutton
Person information
- affiliation: Google Research, Mountain View, CA, USA
- affiliation: University of Edinburgh, School of Informatics
- affiliation: The Alan Turing Institute, London, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c84]Kensen Shi, Joey Hong, Yinlin Deng, Pengcheng Yin, Manzil Zaheer, Charles Sutton:
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. ICLR 2024- [c83]Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton:
A Probabilistic Framework for Modular Continual Learning. ICLR 2024 - [c82]Ansong Ni, Miltiadis Allamanis, Arman Cohan, Yinlin Deng, Kensen Shi, Charles Sutton, Pengcheng Yin:
NExT: Teaching Large Language Models to Reason about Code Execution. ICML 2024 - [i68]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 abs/2404.14662 (2024) - [i67]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 abs/2407.09522 (2024) - 2023
- [j13]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: 240:1-240:113 (2023) - [c81]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: 126-173 - [c80]Haoran Sun, Bo Dai, Charles Sutton, Dale Schuurmans, Hanjun Dai:
Any-scale Balanced Samplers for Discrete Space. ICLR 2023 - [c79]Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin:
Can Large Language Models Reason about Program Invariants? ICML 2023: 27496-27520 - [c78]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 - [c77]Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton:
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. NeurIPS 2023 - [i66]Kensen Shi, Hanjun Dai, Wen-Ding Li, Kevin Ellis, Charles Sutton:
LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas. CoRR abs/2306.02049 (2023) - [i65]Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton:
A Probabilistic Framework for Modular Continual Learning. CoRR abs/2306.06545 (2023) - [i64]Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton:
ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis. CoRR abs/2307.13883 (2023) - [i63]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 abs/2311.17311 (2023) - [i62]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 abs/2312.02179 (2023) - [i61]Michael Pradel, Baishakhi Ray, Charles Sutton, Eran Yahav:
Programming Language Processing (Dagstuhl Seminar 23062). Dagstuhl Reports 13(2): 20-32 (2023) - 2022
- [j12]Maria I. Gorinova, Andrew D. Gordon, Charles Sutton, Matthijs Vákár
:
Conditional Independence by Typing. ACM Trans. Program. Lang. Syst. 44(1): 4:1-4:54 (2022) - [c76]Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton:
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. ICLR 2022 - [e1]Swarat Chaudhuri, Charles Sutton:
MAPS@PLDI 2022: 6th ACM SIGPLAN International Symposium on Machine Programming, San Diego, CA, USA, 13 June 2022. ACM 2022, ISBN 978-1-4503-9273-0 [contents] - [i60]Kensen Shi, Hanjun Dai, Kevin Ellis, Charles Sutton:
CrossBeam: Learning to Search in Bottom-Up Program Synthesis. CoRR abs/2203.10452 (2022) - [i59]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 abs/2204.02311 (2022) - [i58]Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton:
Compositional Generalization and Decomposition in Neural Program Synthesis. CoRR abs/2204.03758 (2022) - [i57]Simão Eduardo, Kai Xu, Alfredo Nazábal, Charles Sutton:
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs. CoRR abs/2207.08050 (2022) - [i56]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 abs/2207.10342 (2022) - [i55]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 abs/2208.07461 (2022) - [i54]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 abs/2212.09248 (2022) - 2021
- [c75]Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge:
Couplings for Multinomial Hamiltonian Monte Carlo. AISTATS 2021: 3646-3654 - [c74]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai:
BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration. ICLR 2021 - [c73]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. ICML 2021: 1661-1672 - [c72]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. ICML 2021: 4308-4318 - [c71]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: 2478-2490 - [c70]Shobha Vasudevan, Wenjie Jiang, David Bieber, Rishabh Singh, Hamid Shojaei, Richard Ho, Charles Sutton:
Learning Semantic Representations to Verify Hardware Designs. NeurIPS 2021: 23491-23504 - [i53]Xinyun Chen, Petros Maniatis, Rishabh Singh, Charles Sutton, Hanjun Dai, Max Lin, Denny Zhou:
SpreadsheetCoder: Formula Prediction from Semi-structured Context. CoRR abs/2106.15339 (2021) - [i52]Jacob Austin, Augustus Odena, Maxwell I. 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 abs/2108.07732 (2021) - [i51]Maxwell I. 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 abs/2112.00114 (2021) - 2020
- [c69]Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton:
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. AISTATS 2020: 4056-4066 - [c68]Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, David Bieber:
Global Relational Models of Source Code. ICLR 2020 - [c67]Augustus Odena, Charles Sutton:
Learning to Represent Programs with Property Signatures. ICLR 2020 - [c66]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Generative Ratio Matching Networks. ICLR 2020 - [c65]Kensen Shi, David Bieber, Charles Sutton:
Incremental Sampling Without Replacement for Sequence Models. ICML 2020: 8785-8795 - [c64]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: 19-20 - [c63]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: 21-24 - [c62]Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes:
Open-vocabulary models for source code. ICSE (Companion Volume) 2020: 294-295 - [c61]Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes:
Big code != big vocabulary: open-vocabulary models for source code. ICSE 2020: 1073-1085 - [c60]Rafael-Michael Karampatsis, Charles Sutton:
How Often Do Single-Statement Bugs Occur?: The ManySStuBs4J Dataset. MSR 2020: 573-577 - [c59]David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow:
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. NeurIPS 2020 - [c58]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. NeurIPS 2020 - [i50]Augustus Odena, Charles Sutton:
Learning to Represent Programs with Property Signatures. CoRR abs/2002.09030 (2020) - [i49]Kensen Shi, David Bieber, Charles Sutton:
Incremental Sampling Without Replacement for Sequence Models. CoRR abs/2002.09067 (2020) - [i48]Daniel A. Abolafia, Rishabh Singh, Manzil Zaheer, Charles Sutton:
Towards Modular Algorithm Induction. CoRR abs/2003.04227 (2020) - [i47]Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, Andrea Janes:
Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code. CoRR abs/2003.07914 (2020) - [i46]Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, Charles Sutton:
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints. CoRR abs/2004.00348 (2020) - [i45]Rafael-Michael Karampatsis, Charles Sutton:
SCELMo: Source Code Embeddings from Language Models. CoRR abs/2004.13214 (2020) - [i44]Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton:
Neural Program Synthesis with a Differentiable Fixer. CoRR abs/2006.10924 (2020) - [i43]Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton:
BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration. CoRR abs/2007.14381 (2020) - [i42]Maria I. Gorinova, Andrew D. Gordon, Charles Sutton, Matthijs Vákár:
Conditional independence by typing. CoRR abs/2010.11887 (2020) - [i41]David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow:
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. CoRR abs/2010.12621 (2020) - [i40]Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans:
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration. CoRR abs/2011.05363 (2020) - [i39]Joey Hong, David Dohan, Rishabh Singh, Charles Sutton, Manzil Zaheer:
Latent Programmer: Discrete Latent Codes for Program Synthesis. CoRR abs/2012.00377 (2020)
2010 – 2019
- 2019
- [j11]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): 1799-1820 (2019) - [j10]Maria I. Gorinova, Andrew D. Gordon, Charles Sutton:
Probabilistic programming with densities in SlicStan: efficient, flexible, and deterministic. Proc. ACM Program. Lang. 3(POPL): 35:1-35:30 (2019) - [c57]Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton:
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. AAAI 2019: 29-36 - [c56]Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton:
GEMSEC: graph embedding with self clustering. ASONAM 2019: 65-72 - [c55]Kai Xu, Akash Srivastava, Charles Sutton:
Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML 2019: 6963-6972 - [c54]Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton:
Learning Semantic Annotations for Tabular Data. IJCAI 2019: 2088-2094 - [i38]Rafael-Michael Karampatsis, Charles Sutton:
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code. CoRR abs/1903.05734 (2019) - [i37]Rafael-Michael Karampatsis, Charles Sutton:
How Often Do Single-Statement Bugs Occur? The ManySStuBs4J Dataset. CoRR abs/1905.13334 (2019) - [i36]Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton:
Learning Semantic Annotations for Tabular Data. CoRR abs/1906.00781 (2019) - [i35]Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton:
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data. CoRR abs/1907.06671 (2019) - [i34]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 abs/1911.01205 (2019) - 2018
- [j9]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): 81:1-81:37 (2018) - [j8]Miltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar T. Devanbu, Mark Marron, Charles Sutton:
Mining Semantic Loop Idioms. IEEE Trans. Software Eng. 44(7): 651-668 (2018) - [c53]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: 2604-2611 - [c52]Nikolaos Katirtzis, Themistoklis Diamantopoulos, Charles Sutton:
Summarizing Software API Usage Examples Using Clustering Techniques. FASE 2018: 189-206 - [c51]Charles Sutton, Timothy Hobson, James Geddes, Rich Caruana:
Data Diff: Interpretable, Executable Summaries of Changes in Distributions for Data Wrangling. KDD 2018: 2279-2288 - [c50]Annie Louis, Charles Sutton:
Deep Dungeons and Dragons: Learning Character-Action Interactions from Role-Playing Game Transcripts. NAACL-HLT (2) 2018: 708-713 - [c49]Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri:
HOUDINI: Lifelong Learning as Program Synthesis. NeurIPS 2018: 8701-8712 - [c48]Tomas Petricek, James Geddes, Charles Sutton:
Wrattler: Reproducible, live and polyglot notebooks. TaPP 2018 - [i33]Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Charles Sutton:
GEMSEC: Graph Embedding with Self Clustering. CoRR abs/1802.03997 (2018) - [i32]Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton:
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge. CoRR abs/1803.04042 (2018) - [i31]Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri:
Synthesis of Differentiable Functional Programs for Lifelong Learning. CoRR abs/1804.00218 (2018) - [i30]Akash Srivastava, Charles Sutton:
Variational Inference In Pachinko Allocation Machines. CoRR abs/1804.07944 (2018) - [i29]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR abs/1806.00101 (2018) - [i28]Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton:
Deep Learning to Detect Redundant Method Comments. CoRR abs/1806.04616 (2018) - [i27]Maria I. Gorinova, Andrew D. Gordon, Charles Sutton:
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic. CoRR abs/1811.00890 (2018) - [i26]Jiaoyan Chen, Ernesto Jiménez-Ruiz, Ian Horrocks, Charles Sutton:
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction. CoRR abs/1811.01304 (2018) - [i25]Gerrit J. J. van den Burg, Alfredo Nazábal, Charles Sutton:
Wrangling Messy CSV Files by Detecting Row and Type Patterns. CoRR abs/1811.11242 (2018) - 2017
- [j7]Jaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton:
Autofolding for Source Code Summarization. IEEE Trans. Software Eng. 43(12): 1095-1109 (2017) - [c47]Akash Srivastava, Charles Sutton:
Autoencoding Variational Inference For Topic Models. ICLR (Poster) 2017 - [c46]Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. ICML 2017: 80-88 - [c45]Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton:
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning. NIPS 2017: 3308-3318 - [i24]Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, Charles Sutton:
A Survey of Machine Learning for Big Code and Naturalness. CoRR abs/1709.06182 (2017) - [i23]Charles Sutton, Linan Gong:
Popularity of arXiv.org within Computer Science. CoRR abs/1710.05225 (2017) - 2016
- [j6]Weikun Wang, Giuliano Casale, Charles Sutton:
A Bayesian Approach to Parameter Inference in Queueing Networks. ACM Trans. Model. Comput. Simul. 27(1): 2 (2016) - [c44]Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik:
Explaining Unwanted Behaviours in Context. IMPS@ESSoS 2016: 38-45 - [c43]Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik:
A text-mining approach to explain unwanted behaviours. EUROSEC 2016: 4:1-4:6 - [c42]Miltiadis Allamanis, Hao Peng, Charles Sutton:
A Convolutional Attention Network for Extreme Summarization of Source Code. ICML 2016: 2091-2100 - [c41]Jaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton:
TASSAL: autofolding for source code summarization. ICSE (Companion Volume) 2016: 649-652 - [c40]Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik:
On Robust Malware Classifiers by Verifying Unwanted Behaviours. IFM 2016: 326-341 - [c39]Daniel Duma, Charles Sutton, Ewan Klein:
Context Matters: Towards Extracting a Citation's Context Using Linguistic Features. JCDL 2016: 201-202 - [c38]Jaroslav M. Fowkes, Charles Sutton:
A Subsequence Interleaving Model for Sequential Pattern Mining. KDD 2016: 835-844 - [c37]Jaroslav M. Fowkes, Charles Sutton:
A Bayesian Network Model for Interesting Itemsets. ECML/PKDD (2) 2016: 410-425 - [c36]Krzysztof J. Geras, Charles Sutton:
Composite Denoising Autoencoders. ECML/PKDD (1) 2016: 681-696 - [c35]Jaroslav M. Fowkes, Charles Sutton:
Parameter-free probabilistic API mining across GitHub. SIGSOFT FSE 2016: 254-265 - [c34]Wei Chen, David Aspinall, Andrew D. Gordon, Charles Sutton, Igor Muttik:
More Semantics More Robust: Improving Android Malware Classifiers. WISEC 2016: 147-158 - [i22]Miltiadis Allamanis, Hao Peng, Charles Sutton:
A Convolutional Attention Network for Extreme Summarization of Source Code. CoRR abs/1602.03001 (2016) - [i21]Jaroslav M. Fowkes, Charles Sutton:
A Subsequence Interleaving Model for Sequential Pattern Mining. CoRR abs/1602.05012 (2016) - [i20]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1602.06886 (2016) - [i19]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1606.05896 (2016) - [i18]Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. CoRR abs/1611.01423 (2016) - [i17]Miltiadis Allamanis, Earl T. Barr, René Just, Charles Sutton:
Tailored Mutants Fit Bugs Better. CoRR abs/1611.02516 (2016) - [i16]Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel H. Goddard, Charles Sutton:
Sequence-to-point learning with neural networks for nonintrusive load monitoring. CoRR abs/1612.09106 (2016) - 2015
- [c33]Mingjun Zhong, Nigel H. Goddard, Charles Sutton:
Latent Bayesian melding for integrating individual and population models. NIPS 2015: 3618-3626 - [c32]Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton:
Suggesting accurate method and class names. ESEC/SIGSOFT FSE 2015: 38-49 - [c31]Krzysztof J. Geras, Charles Sutton:
Scheduled denoising autoencoders. ICLR (Poster) 2015 - [i15]Jaroslav M. Fowkes, Charles Sutton:
A Bayesian Network Model for Interesting Itemsets. CoRR abs/1510.04130 (2015) - [i14]Mingjun Zhong, Nigel H. Goddard, Charles Sutton:
Latent Bayesian melding for integrating individual and population models. CoRR abs/1510.09130 (2015) - [i13]Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana, Gregor Urban, Shengjie Wang, Özlem Aslan, Matthai Philipose, Matthew Richardson, Charles Sutton:
Compressing LSTMs into CNNs. CoRR abs/1511.06433 (2015) - [i12]Jaroslav M. Fowkes, Charles Sutton:
Parameter-Free Probabilistic API Mining at GitHub Scale. CoRR abs/1512.05558 (2015) - [i11]William W. Cohen, Charles Sutton, Martin T. Vechev:
Programming with "Big Code" (Dagstuhl Seminar 15472). Dagstuhl Reports 5(11): 90-102 (2015) - 2014
- [c30]Yichuan Zhang, Charles Sutton:
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models. NIPS 2014: 10-18 - [c29]Mingjun Zhong, Nigel H. Goddard, Charles Sutton:
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation. NIPS 2014: 3590-3598 - [c28]Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton:
Learning natural coding conventions. SIGSOFT FSE 2014: 281-293 - [c27]Miltiadis Allamanis, Charles Sutton:
Mining idioms from source code. SIGSOFT FSE 2014: 472-483 - [c26]Quim Castellà, Charles Sutton:
Word storms: multiples of word clouds for visual comparison of documents. WWW 2014: 665-676 - [i10]Miltiadis Allamanis, Earl T. Barr, Charles Sutton:
Learning Natural Coding Conventions. CoRR abs/1402.4182 (2014) - [i9]Jaroslav M. Fowkes, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton:
Autofolding for Source Code Summarization. CoRR abs/1403.4503 (2014) - [i8]Miltiadis Allamanis, Charles Sutton:
Mining Idioms from Source Code. CoRR abs/1404.0417 (2014) - [i7]Yichuan Zhang, Charles Sutton:
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models. CoRR abs/1406.3843 (2014) - 2013
- [j5]Thanh T. L. Tran, Yanlei Diao, Charles Sutton, Anna Liu:
Supporting User-Defined Functions on Uncertain Data. Proc. VLDB Endow. 6(6): 469-480 (2013) - [c25]Krzysztof J. Geras, Charles Sutton:
Multiple-source cross-validation. ICML (3) 2013: 1292-1300 - [c24]Miltiadis Allamanis, Charles Sutton:
Why, when, and what: analyzing stack overflow questions by topic, type, and code. MSR 2013: 53-56 - [c23]Miltiadis Allamanis, Charles Sutton:
Mining source code repositories at massive scale using language modeling. MSR 2013: 207-216 - [i6]Quim Castellà, Charles Sutton:
Word Storms: Multiples of Word Clouds for Visual Comparison of Documents. CoRR abs/1301.0503 (2013) - 2012
- [j4]Charles Sutton, Andrew McCallum:
An Introduction to Conditional Random Fields. Found. Trends Mach. Learn. 4(4): 267-373 (2012) - [c22]Nigel H. Goddard, Johanna D. Moore, Charles Sutton, Heather Lovell, Janette Webb:
Machine learning and multimedia content generation for energy demand reduction. SustainIT 2012: 1-5 - [c21]Yichuan Zhang, Charles Sutton, Amos J. Storkey, Zoubin Ghahramani:
Continuous Relaxations for Discrete Hamiltonian Monte Carlo. NIPS 2012: 3203-3211 - [i5]Charles Sutton, Andrew McCallum:
Improved Dynamic Schedules for Belief Propagation. CoRR abs/1206.5291 (2012) - [i4]Charles Sutton, Andrew McCallum:
Piecewise Training for Undirected Models. CoRR abs/1207.1409 (2012) - 2011
- [j3]Zhao Cao, Charles Sutton, Yanlei Diao, Prashant J. Shenoy:
Distributed inference and query processing for RFID tracking and monitoring. Proc. VLDB Endow. 4(5): 326-337 (2011) - [c20]Yichuan Zhang, Charles Sutton:
Quasi-Newton Methods for Markov Chain Monte Carlo. NIPS 2011: 2393-2401 - [i3]Zhao Cao, Charles Sutton, Yanlei Diao, Prashant J. Shenoy:
Distributed Inference and Query Processing for RFID Tracking and Monitoring. CoRR abs/1103.4410 (2011) - 2010
- [c19]Charles Sutton, Michael I. Jordan:
Inference and Learning in Networks of Queues. AISTATS 2010: 796-803 - [i2]Charles Sutton, Michael I. Jordan:
Bayesian Inference in Queueing Networks. CoRR abs/1001.3355 (2010)
2000 – 2009
- 2009
- [j2]Charles Sutton, Andrew McCallum:
Piecewise training for structured prediction. Mach. Learn. 77(2-3): 165-194 (2009) - [c18]Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Charles Sutton, Thanh T. L. Tran, Michael Zink:
Capturing Data Uncertainty in High-Volume Stream Processing. CIDR 2009 - [c17]Peter Bodík, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, David A. Patterson:
Statistical Machine Learning Makes Automatic Control Practical for Internet Datacenters. HotCloud 2009 - [c16]Peter Bodík, Rean Griffith, Charles Sutton, Armando Fox, Michael I. Jordan, David A. Patterson:
Automatic exploration of datacenter performance regimes. ACDC@ICAC 2009: 1-6 - [c15]Thanh T. L. Tran, Charles Sutton, Richard Cocci, Yanming Nie, Yanlei Diao, Prashant J. Shenoy:
Probabilistic Inference over RFID Streams in Mobile Environments. ICDE 2009: 1096-1107 - [i1]Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Charles Sutton, Thanh T. L. Tran, Michael Zink:
Capturing Data Uncertainty in High-Volume Stream Processing. CoRR abs/0909.1777 (2009) - 2008
- [c14]Robert J. Hall, Charles Sutton, Andrew McCallum:
Unsupervised deduplication using cross-field dependencies. KDD 2008: 310-317 - [c13]Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. Doug Tygar, Kai Xia:
Exploiting Machine Learning to Subvert Your Spam Filter. LEET 2008 - [c12]Charles Sutton, Michael I. Jordan:
Probabilistic Inference in Queueing Networks. SysML 2008 - 2007
- [j1]Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh:
Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data. J. Mach. Learn. Res. 8: 693-723 (2007) - [c11]Charles Sutton, Andrew McCallum:
Piecewise pseudolikelihood for efficient training of conditional random fields. ICML 2007: 863-870 - [c10]Charles Sutton, Andrew McCallum:
Improved Dynamic Schedules for Belief Propagation. UAI 2007: 376-383 - 2006
- [c9]Chris Pal, Charles Sutton, Andrew McCallum:
Sparse Forward-Backward Using Minimum Divergence Beams for Fast Training Of Conditional Random Fields. ICASSP (5) 2006: 581-584 - [c8]Charles Sutton, Michael Sindelar, Andrew McCallum:
Reducing Weight Undertraining in Structured Discriminative Learning. HLT-NAACL 2006 - 2005
- [c7]Max Welling, Charles Sutton:
Learning in Markov Random Fields with Contrastive Free Energies. AISTATS 2005: 397-404 - [c6]Charles Sutton, Andrew McCallum:
Joint Parsing and Semantic Role Labeling. CoNLL 2005: 225-228 - [c5]Charles Sutton, Andrew McCallum:
Composition of Conditional Random Fields for Transfer Learning. HLT/EMNLP 2005: 748-754 - [c4]Charles Sutton, Andrew McCallum:
Piecewise Training for Undirected Models. UAI 2005: 568-575 - 2004
- [c3]Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum:
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data. ICML 2004 - 2003
- [c2]Paul R. Cohen, Charles A. Sutton:
Very Predictive Ngrams for Space-Limited Probabilistic Models. IDA 2003: 134-142 - [c1]Charles A. Sutton, Brendan Burns, Clayton T. Morrison, Paul R. Cohen:
Guided Incremental Construction of Belief Networks. IDA 2003: 533-543
Coauthor Index
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-09-07 00:58 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
