Evolution of a location-based online social network: analysis and models. - Miltiadis Allamanis; Salvatore Scellato; Cecilia Mascolo (Internet Measurement Conference, 2012)

Effective Rule-Based Multi-label Classification with Learning Classifier Systems. - Miltiadis Allamanis; Fani A. Tzima; Pericles A. Mitkas (ICANNGA, 2013)

Why, when, and what: analyzing stack overflow questions by topic, type, and code. - Miltiadis Allamanis; Charles Sutton (MSR, 2013)

Mining source code repositories at massive scale using language modeling. - Miltiadis Allamanis; Charles Sutton (MSR, 2013)

Learning natural coding conventions. - Miltiadis Allamanis; Earl T. Barr; Christian Bird; Charles Sutton (SIGSOFT FSE, 2014)

Mining idioms from source code. - Miltiadis Allamanis; Charles Sutton (SIGSOFT FSE, 2014)

Learning Natural Coding Conventions. - Miltiadis Allamanis; Earl T. Barr; Charles Sutton (CoRR, 2014)

Autofolding for Source Code Summarization. - Jaroslav M. Fowkes; Razvan Ranca; Miltiadis Allamanis; Mirella Lapata; Charles Sutton (CoRR, 2014)

Bimodal Modelling of Source Code and Natural Language. - Miltiadis Allamanis; Daniel Tarlow; Andrew D. Gordon 001; Yi Wei (ICML, 2015)

Suggesting accurate method and class names. - Miltiadis Allamanis; Earl T. Barr; Christian Bird; Charles Sutton (ESEC/SIGSOFT FSE, 2015)

Inducing Generalized Multi-Label Rules with Learning Classifier Systems. - Fani A. Tzima; Miltiadis Allamanis; Alexandros Filotheou; Pericles A. Mitkas (CoRR, 2015)

A Convolutional Attention Network for Extreme Summarization of Source Code. - Miltiadis Allamanis; Hao Peng; Charles Sutton (ICML, 2016)

TASSAL: autofolding for source code summarization. - Jaroslav M. Fowkes; Pankajan Chanthirasegaran; Razvan Ranca; Miltiadis Allamanis; Mirella Lapata; Charles Sutton (ICSE (Companion Volume), 2016)

Tailored Mutants Fit Bugs Better. - Miltiadis Allamanis; Earl T. Barr; René Just; Charles Sutton (CoRR, 2016)

Learning Continuous Semantic Representations of Symbolic Expressions. - Miltiadis Allamanis; Pankajan Chanthirasegaran; Pushmeet Kohli; Charles Sutton (ICML, 2017)

SmartPaste: Learning to Adapt Source Code. - Miltiadis Allamanis; Marc Brockschmidt (CoRR, 2017)

Autofolding for Source Code Summarization. - Jaroslav M. Fowkes; Pankajan Chanthirasegaran; Razvan Ranca; Miltiadis Allamanis; Mirella Lapata; Charles Sutton (IEEE Trans. Software Eng., 2017)

Learning natural coding conventions - Miltiadis Allamanis (2017)

Learning to Represent Programs with Graphs. - Miltiadis Allamanis; Marc Brockschmidt; Mahmoud Khademi (ICLR, 2018)

Modelling Natural Language, Programs, and their Intersection. - Graham Neubig; Miltiadis Allamanis (NAACL-HLT (Tutorial Abstracts), 2018)

Constrained Graph Variational Autoencoders for Molecule Design. - Qi Liu 049; Miltiadis Allamanis; Marc Brockschmidt; Alexander L. Gaunt (NeurIPS, 2018)

RefiNym: using names to refine types. - Santanu Kumar Dash 001; Miltiadis Allamanis; Earl T. Barr (ESEC/SIGSOFT FSE, 2018)

Deep learning type inference. - Vincent J. Hellendoorn; Christian Bird; Earl T. Barr; Miltiadis Allamanis (ESEC/SIGSOFT FSE, 2018)

Tree2Tree Neural Translation Model for Learning Source Code Changes. - Saikat Chakraborty; Miltiadis Allamanis; Baishakhi Ray (CoRR, 2018)

A Survey of Machine Learning for Big Code and Naturalness. - Miltiadis Allamanis; Earl T. Barr; Premkumar T. Devanbu; Charles Sutton (ACM Comput. Surv., 2018)

Mining Semantic Loop Idioms. - Miltiadis Allamanis; Earl T. Barr; Christian Bird; Premkumar T. Devanbu; Mark Marron; Charles Sutton (IEEE Trans. Software Eng., 2018)

Generative Code Modeling with Graphs. - Marc Brockschmidt; Miltiadis Allamanis; Alexander L. Gaunt; Oleksandr Polozov (ICLR (Poster), 2019)

Structured Neural Summarization. - Patrick Fernandes; Miltiadis Allamanis; Marc Brockschmidt (ICLR (Poster), 2019)

Learning to Represent Edits. - Pengcheng Yin; Graham Neubig; Miltiadis Allamanis; Marc Brockschmidt; Alexander L. Gaunt (ICLR (Poster), 2019)

Learning units-of-measure from scientific code. - Matthew Danish; Miltiadis Allamanis; Marc Brockschmidt; Andrew C. Rice; Dominic Orchard (SE4Science@ICSE, 2019)

DIRE: A Neural Approach to Decompiled Identifier Naming. - Jeremy Lacomis; Pengcheng Yin; Edward J. Schwartz; Miltiadis Allamanis; Claire Le Goues; Graham Neubig; Bogdan Vasilescu (ASE, 2019)

Program Synthesis and Semantic Parsing with Learned Code Idioms. - Eui Chul Richard Shin; Miltiadis Allamanis; Marc Brockschmidt; Alex Polozov (NeurIPS, 2019)

The adverse effects of code duplication in machine learning models of code. - Miltiadis Allamanis (Onward!, 2019)

CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. - Hamel Husain; Ho-Hsiang Wu; Tiferet Gazit; Miltiadis Allamanis; Marc Brockschmidt (CoRR, 2019)

Deduplication Index for Big Code Datasets. - Miltiadis Allamanis (IEEE DataPort, 2019)

Typilus: neural type hints. - Miltiadis Allamanis; Earl T. Barr; Soline Ducousso; Zheng Gao (PLDI, 2020)

Flexeme: untangling commits using lexical flows. - Profir-Petru Pârtachi; Santanu Kumar Dash 001; Miltiadis Allamanis; Earl T. Barr (ESEC/SIGSOFT FSE, 2020)

Learning to Encode and Classify Test Executions. - Foivos Tsimpourlas; Ajitha Rajan; Miltiadis Allamanis (CoRR, 2020)

Copy That! Editing Sequences by Copying Spans. - Sheena Panthaplackel; Miltiadis Allamanis; Marc Brockschmidt (AAAI, 2021)

Fast and Memory-Efficient Neural Code Completion. - Alexey Svyatkovskiy; Sebastian Lee; Anna Hadjitofi; Maik Riechert; Juliana Vicente Franco; Miltiadis Allamanis (MSR, 2021)

Self-Supervised Bug Detection and Repair. - Miltiadis Allamanis; Henry Jackson-Flux; Marc Brockschmidt (NeurIPS, 2021)

Supervised learning over test executions as a test oracle. - Foivos Tsimpourlas; Ajitha Rajan; Miltiadis Allamanis (SAC, 2021)

Learning to Generate Code Sketches. - Daya Guo; Alexey Svyatkovskiy; Jian Yin 001; Nan Duan; Marc Brockschmidt; Miltiadis Allamanis (CoRR, 2021)

CoRGi: Content-Rich Graph Neural Networks with Attention. - Jooyeon Kim; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Cheng Zheng; Miltiadis Allamanis (CoRR, 2021)

VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups. - Pablo Morales-Alvarez; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2021)

Learning to Complete Code with Sketches. - Daya Guo; Alexey Svyatkovskiy; Jian Yin 001; Nan Duan; Marc Brockschmidt; Miltiadis Allamanis (ICLR, 2022)

CoRGi: Content-Rich Graph Neural Networks with Attention. - Jooyeon Kim; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Cheng Zhang 005; Miltiadis Allamanis (KDD, 2022)

NS3: Neuro-symbolic Semantic Code Search. - Shushan Arakelyan; Anna Hakhverdyan; Miltiadis Allamanis; Luis Garcia 001; Christophe Hauser; Xiang Ren (NeurIPS, 2022)

Simultaneous Missing Value Imputation and Structure Learning with Groups. - Pablo Morales-Alvarez; Wenbo Gong 001; Angus Lamb; Simon Woodhead 002; Simon Peyton Jones; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (NeurIPS, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

Is Surprisal in Issue Trackers Actionable? - James Caddy; Markus Wagner 007; Christoph Treude; Earl T. Barr; Miltiadis Allamanis (CoRR, 2022)

AdaptivePaste: Code Adaptation through Learning Semantics-aware Variable Usage Representations. - Xiaoyu Liu; Jinu Jang; Neel Sundaresan; Miltiadis Allamanis; Alexey Svyatkovskiy (CoRR, 2022)

Embedding and classifying test execution traces using neural networks. - Foivos Tsimpourlas; Gwenyth Rooijackers; Ajitha Rajan; Miltiadis Allamanis (IET Softw., 2022)

Overwatch: learning patterns in code edit sequences. - Yuhao Zhang 005; Yasharth Bajpai; Priyanshu Gupta; Ameya Ketkar; Miltiadis Allamanis; Titus Barik; Sumit Gulwani; Arjun Radhakrishna; Mohammad Raza; Gustavo Soares; Ashish Tiwari 001 (Proc. ACM Program. Lang., 2022)

HEAT: Hyperedge Attention Networks. - Dobrik Georgiev; Marc Brockschmidt; Miltiadis Allamanis (Trans. Mach. Learn. Res., 2022)

CODIT: Code Editing With Tree-Based Neural Models. - Saikat Chakraborty; Yangruibo Ding; Miltiadis Allamanis; Baishakhi Ray (IEEE Trans. Software Eng., 2022)

AdaptivePaste: Intelligent Copy-Paste in IDE. - Xiaoyu Liu; Jinu Jang; Neel Sundaresan; Miltiadis Allamanis; Alexey Svyatkovskiy (ESEC/SIGSOFT FSE, 2023)

Epicure: Distilling Sequence Model Predictions into Patterns. - Miltiadis Allamanis; Earl T. Barr (CoRR, 2023)

JEMMA: An extensible Java dataset for ML4Code applications. - Anjan Karmakar; Miltiadis Allamanis; Romain Robbes (Empir. Softw. Eng., 2023)

DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class. - Luke Dramko; Jeremy Lacomis; Pengcheng Yin; Edward J. Schwartz; Miltiadis Allamanis; Graham Neubig; Bogdan Vasilescu; Claire Le Goues (ACM Trans. Softw. Eng. Methodol., 2023)

Unsupervised Evaluation of Code LLMs with Round-Trip Correctness. - Miltiadis Allamanis; Sheena Panthaplackel; Pengcheng Yin (ICML, 2024)

Do Large Code Models Understand Programming Concepts? Counterfactual Analysis for Code Predicates. - Ashish Hooda; Mihai Christodorescu; Miltiadis Allamanis; Aaron Wilson; Kassem Fawaz; Somesh Jha (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 (ICML, 2024)

Do Large Code Models Understand Programming Concepts? A Black-box Approach. - Ashish Hooda; Mihai Christodorescu; Miltiadis Allamanis; Aaron Wilson; Kassem Fawaz; Somesh Jha (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)

Disproving Program Equivalence with LLMs. - Miltiadis Allamanis; Pengcheng Yin (CoRR, 2025)