Scalable diffusion-aware optimization of network topology. - Elias Boutros Khalil; Bistra Dilkina; Le Song (KDD, 2014)

Large-scale insider trading analysis: patterns and discoveries. - Acar Tamersoy; Elias B. Khalil; Bo Xie 002; Stephen L. Lenkey; Bryan R. Routledge; Duen Horng Chau; Shamkant B. Navathe (Soc. Netw. Anal. Min., 2014)

Learning to Branch in Mixed Integer Programming. - Elias Boutros Khalil; Pierre Le Bodic; Le Song; George L. Nemhauser; Bistra Dilkina (AAAI, 2016)

Machine Learning for Integer Programming. - Elias B. Khalil (IJCAI, 2016)

Generating Graph Snapshots from Streaming Edge Data. - Sucheta Soundarajan; Acar Tamersoy; Elias B. Khalil; Tina Eliassi-Rad; Duen Horng Chau; Brian Gallagher; Kevin A. Roundy (WWW (Companion Volume), 2016)

CP-ORTHO: An Orthogonal Tensor Factorization Framework for Spatio-Temporal Data. - Ardavan Afshar; Joyce C. Ho; Bistra Dilkina; Ioakeim Perros; Elias B. Khalil; Li Xiong 001; Vaidy S. Sunderam (SIGSPATIAL/GIS, 2017)

Fake News Mitigation via Point Process Based Intervention. - Mehrdad Farajtabar; Jiachen Yang; Xiaojing Ye; Huan Xu; Rakshit Trivedi; Elias B. Khalil; Shuang Li 002; Le Song; Hongyuan Zha (ICML, 2017)

Learning to Run Heuristics in Tree Search. - Elias B. Khalil; Bistra Dilkina; George L. Nemhauser; Shabbir Ahmed 001; Yufen Shao (IJCAI, 2017)

Learning Feature Engineering for Classification. - Fatemeh Nargesian; Horst Samulowitz; Udayan Khurana; Elias B. Khalil; Deepak S. Turaga (IJCAI, 2017)

Learning Combinatorial Optimization Algorithms over Graphs. - Elias B. Khalil; Hanjun Dai; Yuyu Zhang; Bistra Dilkina; Le Song (NIPS, 2017)

Human Resource Optimization for Bug Fixing: Balancing Short-Term and Long-Term Objectives. - Elias B. Khalil; Mustafa Assaf; Abdel Salam Sayyad (SSBSE, 2017)

Combinatorial Attacks on Binarized Neural Networks. - Elias B. Khalil; Amrita Gupta; Bistra Dilkina (ICLR (Poster), 2019)

Hybrid Models for Learning to Branch. - Prateek Gupta; Maxime Gasse; Elias B. Khalil; Pawan Kumar Mudigonda; Andrea Lodi 001; Yoshua Bengio (NeurIPS, 2020)

Hybrid Models for Learning to Branch. - Prateek Gupta; Maxime Gasse; Elias B. Khalil; M. Pawan Kumar; Andrea Lodi 001; Yoshua Bengio (CoRR, 2020)

Towards Tighter Integration of Machine Learning and Discrete Optimization. - Elias Boutros Khalil (2020)

Learning to Schedule Heuristics in Branch and Bound. - Antonia Chmiela; Elias B. Khalil; Ambros M. Gleixner; Andrea Lodi 001; Sebastian Pokutta (NeurIPS, 2021)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. - Maxime Gasse; Simon Bowly; Quentin Cappart; Jonas Charfreitag; Laurent Charlin; Didier Chételat; Antonia Chmiela; Justin Dumouchelle; Ambros M. Gleixner; Aleksandr M. Kazachkov; Elias B. Khalil; Pawel Lichocki; et al. (NeurIPS (Competition and Demos), 2021)

Learning to Schedule Heuristics in Branch-and-Bound. - Antonia Chmiela; Elias B. Khalil; Ambros M. Gleixner; Andrea Lodi 001; Sebastian Pokutta (Zenodo, 2021)

MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers. - Elias B. Khalil; Christopher Morris 001; Andrea Lodi 001 (AAAI, 2022)

Finding Backdoors to Integer Programs: A Monte Carlo Tree Search Framework. - Elias B. Khalil; Pashootan Vaezipoor; Bistra Dilkina (AAAI, 2022)

Note: Image-based Prediction of House Attributes with Deep Learning. - Weimin Huang 004; Alex Olson; Elias B. Khalil; Shoshanna Saxe (COMPASS, 2022)

A Deep Reinforcement Learning Framework for Column Generation. - Cheng Chi; Amine Mohamed Aboussalah; Elias B. Khalil; Juyoung Wang; Zoha Sherkat-Masoumi (NeurIPS, 2022)

Neur2SP: Neural Two-Stage Stochastic Programming. - Rahul Patel; Justin Dumouchelle; Elias B. Khalil; Merve Bodur (NeurIPS, 2022)

Goose: A Meta-Solver for Deep Neural Network Verification. - Joseph Scott; Guanting Pan; Elias B. Khalil; Vijay Ganesh 001 (SMT, 2022)

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. - Maxime Gasse; Quentin Cappart; Jonas Charfreitag; Laurent Charlin; Didier Chételat; Antonia Chmiela; Justin Dumouchelle; Ambros M. Gleixner; Aleksandr M. Kazachkov; Elias B. Khalil; Pawel Lichocki; Andrea Lodi 001; et al. (CoRR, 2022)

Machine learning using preoperative patient factors can predict duration of surgery and length of stay for total knee arthroplasty. - Aazad Abbas; Jacob Mosseri; Johnathan R. Lex; Jay Toor; Bheeshma Ravi; Elias B. Khalil; Cari M. Whyne (Int. J. Medical Informatics, 2022)

Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach. - Mohammad Ali Alomrani; Reza Moravej; Elias Boutros Khalil (Trans. Mach. Learn. Res., 2022)

Lookback for Learning to Branch. - Prateek Gupta; Elias Boutros Khalil; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Yoshua Bengio; M. Pawan Kumar (Trans. Mach. Learn. Res., 2022)

Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto. - Weimin Huang; Elias B. Khalil (AAAI, 2023)

Recent Developments in Data-Driven Algorithms for Discrete Optimization. - Elias B. Khalil (AAAI, 2023)

Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus. - Yudong Xu 001; Elias B. Khalil; Scott Sanner (AAAI, 2023)

Fast Matrix Multiplication Without Tears: A Constraint Programming Approach. - Arnaud Deza; Chang Liu; Pashootan Vaezipoor; Elias B. Khalil (CP, 2023)

Scalable and Near-Optimal ε-Tube Clusterwise Regression. - Aravinth Chembu; Scott Sanner; Elias B. Khalil (CPAIOR, 2023)

Machine Learning for Cutting Planes in Integer Programming: A Survey. - Arnaud Deza; Elias B. Khalil (IJCAI, 2023)

Multi-task Predict-then-Optimize. - Bo Tang 017; Elias B. Khalil (LION, 2023)

SMAC-tuned Deep Q-learning for Ramp Metering. - Omar ElSamadisy; Yazeed Abdulhai; Haoyuan Xue; Ilia Smirnov; Elias B. Khalil; Baher Abdulhai (SM, 2023)

Pierce: A Testing Tool for Neural Network Verification Solvers. - Joseph Scott; Guanting Pan; Piyush Jha; Elias B. Khalil; Vijay Ganesh 001 (VSTTE, 2023)

Combinatorial Optimization and Reasoning with Graph Neural Networks. - Quentin Cappart; Didier Chételat; Elias B. Khalil; Andrea Lodi 001; Christopher Morris 001; Petar Velickovic (J. Mach. Learn. Res., 2023)

LEO: Learning Efficient Orderings for Multiobjective Binary Decision Diagrams. - Rahul Patel; Elias B. Khalil (CPAIOR (2), 2024)

CaVE: A Cone-Aligned Approach for Fast Predict-then-optimize with Binary Linear Programs. - Bo Tang 017; Elias B. Khalil (CPAIOR (2), 2024)

Neur2RO: Neural Two-Stage Robust Optimization. - Justin Dumouchelle; Esther Julien; Jannis Kurtz; Elias Boutros Khalil (ICLR, 2024)

Neur2BiLO: Neural Bilevel Optimization. - Justin Dumouchelle; Esther Julien; Jannis Kurtz; Elias B. Khalil (NeurIPS, 2024)

MORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning to Sparsify. - Rahul Patel; Elias B. Khalil; David Bergman (CoRR, 2024)

Learning to Optimize for Mixed-Integer Non-linear Programming. - Bo Tang 017; Elias B. Khalil; Ján Drgona (CoRR, 2024)

LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations. - Yudong Xu 001; Wenhao Li; Pashootan Vaezipoor; Scott Sanner; Elias Boutros Khalil (Trans. Mach. Learn. Res., 2024)

PyEPO: a PyTorch-based end-to-end predict-then-optimize library for linear and integer programming. - Bo Tang 017; Elias B. Khalil (Math. Program. Comput., 2024)

Learn2Aggregate: Supervised Generation of Chvatal-Gomory Cuts Using Graph Neural Networks. - Arnaud Deza; Elias B. Khalil; Zhenan Fan; Zirui Zhou; Yong Zhang 004 (AAAI, 2025)

Reinforcement learning with combinatorial actions for coupled restless bandits. - Lily Xu; Bryan Wilder; Elias Boutros Khalil; Milind Tambe (ICLR, 2025)

Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects. - Wenhao Li; Yudong Xu 001; Scott Sanner; Elias Boutros Khalil (Trans. Mach. Learn. Res., 2025)

Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction. - Yudong W. Xu; Wenhao Li; Scott Sanner; Elias B. Khalil (CoRR, 2025)