Miltiadis Allamanis - researchr profile publications

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Publications with a 'Miltiadis Allamanis' as author, editor, or advisor:

CODIT: Code Editing With Tree-Based Neural ModelsSaikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, Baishakhi Ray. TSE, 48(4):1385-1399, 2022. [doi]
Deep learning type inferenceVincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis. FSE 2018: 152-162 [doi]
Learning to Represent Programs with GraphsMiltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi. iclr 2018: [doi]
Overwatch: learning patterns in code edit sequencesYuhao Zhang, Yasharth Bajpai, Priyanshu Gupta, Ameya Ketkar, Miltiadis Allamanis, Titus Barik, Sumit Gulwani, Arjun Radhakrishna, Mohammad Raza, Gustavo Soares, Ashish Tiwari 0001. PACMPL, 6(OOPSLA2):395-423, 2022. [doi]
Simultaneous Missing Value Imputation and Structure Learning with GroupsPablo Morales-Alvarez, Wenbo Gong 0001, Angus Lamb, Simon Woodhead 0002, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang 0005. nips 2022: [doi]
AdaptivePaste: Intelligent Copy-Paste in IDEXiaoyu Liu, Jinu Jang, Neel Sundaresan, Miltiadis Allamanis, Alexey Svyatkovskiy. FSE 2023: 1844-1854 [doi]
JEMMA: An extensible Java dataset for ML4Code applicationsAnjan Karmakar, Miltiadis Allamanis, Romain Robbes. ese, 28(2):54, March 2023. [doi]
Learning to Complete Code with SketchesDaya Guo, Alexey Svyatkovskiy, Jian Yin 0001, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis. iclr 2022: [doi]
Flexeme: untangling commits using lexical flowsProfir-Petru Pârtachi, Santanu Kumar Dash, Miltiadis Allamanis, Earl T. Barr. FSE 2020: 63-74 [doi]
HEAT: Hyperedge Attention NetworksDobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis. tmlr, 2022, 2022. [doi]
Constrained Graph Variational Autoencoders for Molecule DesignQi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt. nips 2018: 7806-7815 [doi]
Supervised learning over test executions as a test oracleFoivos Tsimpourlas, Ajitha Rajan, Miltiadis Allamanis. SAC 2021: 1521-1531 [doi]
Self-Supervised Bug Detection and RepairMiltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt. nips 2021: 27865-27876 [doi]
RefiNym: using names to refine typesSantanu Kumar Dash, Miltiadis Allamanis, Earl T. Barr. FSE 2018: 107-117 [doi]
DIRE: A Neural Approach to Decompiled Identifier NamingJeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu. ASE 2019: 628-639 [doi]
Learning units-of-measure from scientific codeMatthew Danish, Miltiadis Allamanis, Marc Brockschmidt, Andrew C. Rice, Dominic Orchard. ICSE 2019: 43-46 [doi]
Fast and Memory-Efficient Neural Code CompletionAlexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltiadis Allamanis. msr 2021: 329-340 [doi]
Mining Semantic Loop IdiomsMiltiadis Allamanis, Earl T. Barr, Christian Bird, Premkumar T. Devanbu, Mark Marron, Charles A. Sutton. TSE, 44(7):651-668, 2018. [doi]
Suggesting accurate method and class namesMiltiadis Allamanis, Earl T. Barr, Christian Bird, Charles A. Sutton. FSE 2015: 38-49 [doi]
Learning Continuous Semantic Representations of Symbolic ExpressionsMiltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles A. Sutton. icml 2017: 80-88 [doi]
Evolution of a location-based online social network: analysis and modelsMiltiadis Allamanis, Salvatore Scellato, Cecilia Mascolo. imc 2012: 145-158 [doi]
Typilus: neural type hintsMiltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao. PLDI 2020: 91-105 [doi]
NS3: Neuro-symbolic Semantic Code SearchShushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, Xiang Ren. nips 2022: [doi]
The adverse effects of code duplication in machine learning models of codeMiltiadis Allamanis. OOPSLA 2019: 143-153 [doi]
Program Synthesis and Semantic Parsing with Learned Code IdiomsEui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov. nips 2019: 10824-10834 [doi]
Generative Code Modeling with GraphsMarc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov. iclr 2019: [doi]
CoRGi: Content-Rich Graph Neural Networks with AttentionJooyeon Kim, Angus Lamb, Simon Woodhead 0002, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis. kdd 2022: 773-783 [doi]
Mining source code repositories at massive scale using language modelingMiltiadis Allamanis, Charles A. Sutton. msr 2013: 207-216 [doi]
Copy That! Editing Sequences by Copying SpansSheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt. AAAI 2021: 13622-13630 [doi]
Autofolding for Source Code SummarizationJaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles A. Sutton. TSE, 43(12):1095-1109, 2017. [doi]
Embedding and classifying test execution traces using neural networksFoivos Tsimpourlas, Gwenyth Rooijackers, Ajitha Rajan, Miltiadis Allamanis. iet-sen, 16(3):301-316, 2022. [doi]
A Survey of Machine Learning for Big Code and NaturalnessMiltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, Charles A. Sutton. ACM Comput. Surv., 51(4), 2018. [doi]
Learning natural coding conventionsMiltiadis Allamanis, Earl T. Barr, Christian Bird, Charles A. Sutton. FSE 2014: 281-293 [doi]
DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software ClassLuke Dramko, Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Graham Neubig, Bogdan Vasilescu, Claire Le Goues. TOSEM, 32(2), April 2023. [doi]
Structured Neural SummarizationPatrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt. iclr 2019: [doi]
Modelling Natural Language, Programs, and their IntersectionGraham Neubig, Miltiadis Allamanis. naacl 2018: 1-3 [doi]
TASSAL: autofolding for source code summarizationJaroslav M. Fowkes, Pankajan Chanthirasegaran, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles A. Sutton. ICSE 2016: 649-652 [doi]
Effective Rule-Based Multi-label Classification with Learning Classifier SystemsMiltiadis Allamanis, Fani A. Tzima, Pericles A. Mitkas. icannga 2013: 466-476 [doi]
Why, when, and what: analyzing stack overflow questions by topic, type, and codeMiltiadis Allamanis, Charles A. Sutton. msr 2013: 53-56 [doi]
Mining idioms from source codeMiltiadis Allamanis, Charles A. Sutton. FSE 2014: 472-483 [doi]
Learning to Represent EditsPengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt. iclr 2019: [doi]
A Convolutional Attention Network for Extreme Summarization of Source CodeMiltiadis Allamanis, Hao Peng, Charles A. Sutton. icml 2016: 2091-2100 [doi]

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