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2020 – today
- 2024
[j10]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024)- [c37]Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin:
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness. ICML 2024 - [c36]Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha:
Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates. ICML 2024 - [c35]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 - [i38]Ashish Hooda, Mihai Christodorescu, Miltiadis Allamanis, Aaron Wilson, Kassem Fawaz, Somesh Jha:
Do Large Code Models Understand Programming Concepts? A Black-box Approach. CoRR abs/2402.05980 (2024) - [i37]Miltiadis Allamanis, Sheena Panthaplackel, Pengcheng Yin:
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness. CoRR abs/2402.08699 (2024) - [i36]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) - 2023
- [j9]Anjan Karmakar
, Miltiadis Allamanis, Romain Robbes:
JEMMA: An extensible Java dataset for ML4Code applications. Empir. Softw. Eng. 28(2): 54 (2023) - [j8]Luke Dramko, Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Graham Neubig, Bogdan Vasilescu, Claire Le Goues:
DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class. ACM Trans. Softw. Eng. Methodol. 32(2): 39:1-39:34 (2023) - [c34]Xiaoyu Liu, Jinu Jang, Neel Sundaresan, Miltiadis Allamanis, Alexey Svyatkovskiy:
AdaptivePaste: Intelligent Copy-Paste in IDE. ESEC/SIGSOFT FSE 2023: 1844-1854 - [i35]Miltiadis Allamanis, Earl T. Barr:
Epicure: Distilling Sequence Model Predictions into Patterns. CoRR abs/2308.08203 (2023) - 2022
- [j7]Foivos Tsimpourlas, Gwenyth Rooijackers, Ajitha Rajan, Miltiadis Allamanis:
Embedding and classifying test execution traces using neural networks. IET Softw. 16(3): 301-316 (2022) - [j6]Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta, Ameya Ketkar, Miltiadis Allamanis, Titus Barik, Sumit Gulwani, Arjun Radhakrishna, Mohammad Raza, Gustavo Soares, Ashish Tiwari:
Overwatch: learning patterns in code edit sequences. Proc. ACM Program. Lang. 6(OOPSLA2): 395-423 (2022) - [j5]Dobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis:
HEAT: Hyperedge Attention Networks. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, Baishakhi Ray:
CODIT: Code Editing With Tree-Based Neural Models. IEEE Trans. Software Eng. 48(4): 1385-1399 (2022) - [c33]Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis:
Learning to Complete Code with Sketches. ICLR 2022 - [c32]Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zhang, Miltiadis Allamanis:
CoRGi: Content-Rich Graph Neural Networks with Attention. KDD 2022: 773-783 - [c31]Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Luis Garcia, Christophe Hauser, Xiang Ren:
NS3: Neuro-symbolic Semantic Code Search. NeurIPS 2022 - [c30]Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Simultaneous Missing Value Imputation and Structure Learning with Groups. NeurIPS 2022 - [i34]Dobrik Georgiev, Marc Brockschmidt, Miltiadis Allamanis:
HEAT: Hyperedge Attention Networks. CoRR abs/2201.12113 (2022) - [i33]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. CoRR abs/2202.02195 (2022) - [i32]James Caddy, Markus Wagner, Christoph Treude, Earl T. Barr, Miltiadis Allamanis:
Is Surprisal in Issue Trackers Actionable? CoRR abs/2204.07363 (2022) - [i31]Shushan Arakelyan, Anna Hakhverdyan, Miltiadis Allamanis, Christophe Hauser, Luis Garcia, Xiang Ren:
NS3: Neuro-Symbolic Semantic Code Search. CoRR abs/2205.10674 (2022) - [i30]Xiaoyu Liu, Jinu Jang, Neel Sundaresan, Miltiadis Allamanis, Alexey Svyatkovskiy:
AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations. CoRR abs/2205.11023 (2022) - [i29]Yuhao Zhang, Yasharth Bajpai, Priyanshu Gupta, Ameya Ketkar, Miltiadis Allamanis, Titus Barik, Sumit Gulwani, Arjun Radhakrishna, Mohammad Raza, Gustavo Soares, Ashish Tiwari:
Overwatch: Learning Patterns in Code Edit Sequences. CoRR abs/2207.12456 (2022) - [i28]Anjan Karmakar, Miltiadis Allamanis, Romain Robbes:
JEMMA: An Extensible Java Dataset for ML4Code Applications. CoRR abs/2212.09132 (2022) - 2021
- [c29]Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt:
Copy That! Editing Sequences by Copying Spans. AAAI 2021: 13622-13630 - [c28]Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, Miltiadis Allamanis:
Fast and Memory-Efficient Neural Code Completion. MSR 2021: 329-340 - [c27]Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt:
Self-Supervised Bug Detection and Repair. NeurIPS 2021: 27865-27876 - [c26]Foivos Tsimpourlas, Ajitha Rajan, Miltiadis Allamanis:
Supervised learning over test executions as a test oracle. SAC 2021: 1521-1531 - [i27]Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt:
Self-Supervised Bug Detection and Repair. CoRR abs/2105.12787 (2021) - [i26]Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, Miltiadis Allamanis:
Learning to Generate Code Sketches. CoRR abs/2106.10158 (2021) - [i25]Jooyeon Kim, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Cheng Zheng, Miltiadis Allamanis:
CoRGi: Content-Rich Graph Neural Networks with Attention. CoRR abs/2110.04866 (2021) - [i24]Pablo Morales-Alvarez, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Miltiadis Allamanis, Cheng Zhang:
VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups. CoRR abs/2110.08223 (2021) - 2020
- [c25]Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao:
Typilus: neural type hints. PLDI 2020: 91-105 - [c24]Profir-Petru Pârtachi, Santanu Kumar Dash, Miltiadis Allamanis, Earl T. Barr:
Flexeme: untangling commits using lexical flows. ESEC/SIGSOFT FSE 2020: 63-74 - [i23]Foivos Tsimpourlas, Ajitha Rajan, Miltiadis Allamanis:
Learning to Encode and Classify Test Executions. CoRR abs/2001.02444 (2020) - [i22]Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao:
Typilus: Neural Type Hints. CoRR abs/2004.10657 (2020) - [i21]Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Franco, Miltiadis Allamanis:
Fast and Memory-Efficient Neural Code Completion. CoRR abs/2004.13651 (2020) - [i20]Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt:
Copy that! Editing Sequences by Copying Spans. CoRR abs/2006.04771 (2020)
2010 – 2019
- 2019
- [c23]Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov:
Generative Code Modeling with Graphs. ICLR (Poster) 2019 - [c22]Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt:
Structured Neural Summarization. ICLR (Poster) 2019 - [c21]Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Learning to Represent Edits. ICLR (Poster) 2019 - [c20]Matthew Danish, Miltiadis Allamanis, Marc Brockschmidt, Andrew C. Rice, Dominic Orchard:
Learning units-of-measure from scientific code. SE4Science@ICSE 2019: 43-46 - [c19]Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu:
DIRE: A Neural Approach to Decompiled Identifier Naming. ASE 2019: 628-639 - [c18]Eui Chul Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Alex Polozov:
Program Synthesis and Semantic Parsing with Learned Code Idioms. NeurIPS 2019: 10824-10834 - [c17]Miltiadis Allamanis:
The adverse effects of code duplication in machine learning models of code. Onward! 2019: 143-153 - [d1]Miltiadis Allamanis:
Deduplication Index for Big Code Datasets. IEEE DataPort, 2019
- [i19]Richard Shin, Miltiadis Allamanis, Marc Brockschmidt, Oleksandr Polozov:
Program Synthesis and Semantic Parsing with Learned Code Idioms. CoRR abs/1906.10816 (2019) - [i18]Jeremy Lacomis, Pengcheng Yin, Edward J. Schwartz, Miltiadis Allamanis, Claire Le Goues, Graham Neubig, Bogdan Vasilescu:
DIRE: A Neural Approach to Decompiled Identifier Naming. CoRR abs/1909.09029 (2019) - [i17]Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, Marc Brockschmidt:
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. CoRR abs/1909.09436 (2019) - 2018
- [j3]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) - [j2]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) - [c16]Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi:
Learning to Represent Programs with Graphs. ICLR 2018 - [c15]Graham Neubig, Miltiadis Allamanis:
Modelling Natural Language, Programs, and their Intersection. NAACL-HLT (Tutorial Abstracts) 2018: 1-3 - [c14]Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Constrained Graph Variational Autoencoders for Molecule Design. NeurIPS 2018: 7806-7815 - [c13]Santanu Kumar Dash, Miltiadis Allamanis, Earl T. Barr:
RefiNym: using names to refine types. ESEC/SIGSOFT FSE 2018: 107-117 - [c12]Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, Miltiadis Allamanis:
Deep learning type inference. ESEC/SIGSOFT FSE 2018: 152-162 - [i16]Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov:
Generative Code Modeling with Graphs. CoRR abs/1805.08490 (2018) - [i15]Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Constrained Graph Variational Autoencoders for Molecule Design. CoRR abs/1805.09076 (2018) - [i14]Saikat Chakraborty, Miltiadis Allamanis, Baishakhi Ray:
Tree2Tree Neural Translation Model for Learning Source Code Changes. CoRR abs/1810.00314 (2018) - [i13]Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Learning to Represent Edits. CoRR abs/1810.13337 (2018) - [i12]Patrick Fernandes, Miltiadis Allamanis, Marc Brockschmidt:
Structured Neural Summarization. CoRR abs/1811.01824 (2018) - [i11]Miltiadis Allamanis:
The Adverse Effects of Code Duplication in Machine Learning Models of Code. CoRR abs/1812.06469 (2018) - 2017
- [b1]Miltiadis Allamanis:
Learning natural coding conventions. University of Edinburgh, UK, 2017 - [j1]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) - [c11]Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. ICML 2017: 80-88 - [i10]Miltiadis Allamanis, Marc Brockschmidt:
SmartPaste: Learning to Adapt Source Code. CoRR abs/1705.07867 (2017) - [i9]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) - [i8]Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi:
Learning to Represent Programs with Graphs. CoRR abs/1711.00740 (2017) - 2016
- [c10]Miltiadis Allamanis, Hao Peng, Charles Sutton:
A Convolutional Attention Network for Extreme Summarization of Source Code. ICML 2016: 2091-2100 - [c9]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 - [i7]Miltiadis Allamanis, Hao Peng, Charles Sutton:
A Convolutional Attention Network for Extreme Summarization of Source Code. CoRR abs/1602.03001 (2016) - [i6]Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli, Charles Sutton:
Learning Continuous Semantic Representations of Symbolic Expressions. CoRR abs/1611.01423 (2016) - [i5]Miltiadis Allamanis, Earl T. Barr, René Just, Charles Sutton:
Tailored Mutants Fit Bugs Better. CoRR abs/1611.02516 (2016) - 2015
- [c8]Miltiadis Allamanis, Daniel Tarlow, Andrew D. Gordon, Yi Wei:
Bimodal Modelling of Source Code and Natural Language. ICML 2015: 2123-2132 - [c7]Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton:
Suggesting accurate method and class names. ESEC/SIGSOFT FSE 2015: 38-49 - [i4]Fani A. Tzima, Miltiadis Allamanis, Alexandros Filotheou, Pericles A. Mitkas:
Inducing Generalized Multi-Label Rules with Learning Classifier Systems. CoRR abs/1512.07982 (2015) - 2014
- [c6]Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton:
Learning natural coding conventions. SIGSOFT FSE 2014: 281-293 - [c5]Miltiadis Allamanis, Charles Sutton:
Mining idioms from source code. SIGSOFT FSE 2014: 472-483 - [i3]Miltiadis Allamanis, Earl T. Barr, Charles Sutton:
Learning Natural Coding Conventions. CoRR abs/1402.4182 (2014) - [i2]Jaroslav M. Fowkes, Razvan Ranca, Miltiadis Allamanis, Mirella Lapata, Charles Sutton:
Autofolding for Source Code Summarization. CoRR abs/1403.4503 (2014) - [i1]Miltiadis Allamanis, Charles Sutton:
Mining Idioms from Source Code. CoRR abs/1404.0417 (2014) - 2013
- [c4]Miltiadis Allamanis, Fani A. Tzima, Pericles A. Mitkas:
Effective Rule-Based Multi-label Classification with Learning Classifier Systems. ICANNGA 2013: 466-476 - [c3]Miltiadis Allamanis, Charles Sutton:
Why, when, and what: analyzing stack overflow questions by topic, type, and code. MSR 2013: 53-56 - [c2]Miltiadis Allamanis, Charles Sutton:
Mining source code repositories at massive scale using language modeling. MSR 2013: 207-216 - 2012
- [c1]Miltiadis Allamanis, Salvatore Scellato, Cecilia Mascolo:
Evolution of a location-based online social network: analysis and models. Internet Measurement Conference 2012: 145-158
Coauthor Index
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