Prize-Collecting TSP with a Budget Constraint. - Alice Paul; Daniel Freund 001; Aaron M. Ferber; David B. Shmoys; David P. Williamson (ESA, 2017)

MIPaaL: Mixed Integer Program as a Layer. - Aaron M. Ferber; Bryan Wilder; Bistra Dilkina; Milind Tambe (AAAI, 2020)

Budgeted Prize-Collecting Traveling Salesman and Minimum Spanning Tree Problems. - Alice Paul; Daniel Freund 001; Aaron M. Ferber; David B. Shmoys; David P. Williamson (Math. Oper. Res., 2020)

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. - Umang Gupta; Aaron M. Ferber; Bistra Dilkina; Greg Ver Steeg (AAAI, 2021)

Learning Pseudo-Backdoors for Mixed Integer Programs. - Aaron M. Ferber; Jialin Song; Bistra Dilkina; Yisong Yue (SOCS, 2021)

Predicting Wildlife Trafficking Routes with Differentiable Shortest Paths. - Aaron M. Ferber; Emily Griffin; Bistra Dilkina; Burcu B. Keskin; Meredith Gore (CPAIOR, 2023)

Local Branching Relaxation Heuristics for Integer Linear Programs. - Taoan Huang; Aaron M. Ferber; Yuandong Tian; Bistra Dilkina; Benoit Steiner (CPAIOR, 2023)

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. - Aaron M. Ferber; Taoan Huang; Daochen Zha; Martin Schubert; Benoit Steiner; Bistra Dilkina; Yuandong Tian (ICML, 2023)

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. - Taoan Huang; Aaron M. Ferber; Yuandong Tian; Bistra Dilkina; Benoit Steiner (ICML, 2023)

Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. - Arman Zharmagambetov; Brandon Amos; Aaron M. Ferber; Taoan Huang; Bistra Dilkina; Yuandong Tian (NeurIPS, 2023)

GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature. - Aaron M. Ferber; Arman Zharmagambetov; Taoan Huang; Bistra Dilkina; Yuandong Tian (CoRR, 2023)

Erratum to "Budgeted Prize-Collecting Traveling Salesman and Minimum Spanning Tree Problems". - Alice Paul; Daniel Freund 001; Aaron M. Ferber; David B. Shmoys; David P. Williamson (Math. Oper. Res., 2023)

Learning Lagrangian Multipliers for the Travelling Salesman Problem. - Augustin Parjadis; Quentin Cappart; Bistra Dilkina; Aaron M. Ferber; Louis-Martin Rousseau (CP, 2024)

GenCO: Generating Diverse Designs with Combinatorial Constraints. - Aaron M. Ferber; Arman Zharmagambetov; Taoan Huang; Bistra Dilkina; Yuandong Tian (ICML, 2024)

Contrastive Predict-and-Search for Mixed Integer Linear Programs. - Taoan Huang; Aaron M. Ferber; Arman Zharmagambetov; Yuandong Tian; Bistra Dilkina (ICML, 2024)

Critic Loss for Image Classification. - Brendan Hogan Rappazzo; Aaron M. Ferber; Carla Gomes 001 (ICMLA, 2024)

GEM-RAG: Graphical Eigen Memories for Retrieval Augmented Generation. - Brendan Hogan Rappazzo; Yingheng Wang; Aaron M. Ferber; Carla Gomes 001 (ICMLA, 2024)

Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods. - Weimin Huang; Taoan Huang; Aaron M. Ferber; Bistra Dilkina (CoRR, 2024)

AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification. - Brendan Hogan; Anmol Kabra; Felipe Siqueira Pacheco; Laura Greenstreet; Joshua Fan 002; Aaron M. Ferber; Marta Ummus; Alecsander Brito; Olivia J. Graham; Lillian R. Aoki; Drew Harvell; Alex Flecker; et al. (CoRR, 2024)

Constraint-aware Pareto Optimization for Tree-Structured Networks: Addressing Decarbonization Targets with Hydropower Expansion. - Marc Grimson; Zhongdi Qu; Yue Mao; Aaron M. Ferber; Felipe Siqueira Pacheco; Sebastian Heilpern; Hector Angarita; Alexander Flecker; Carla P. Gomes (AAAI, 2025)

Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. - Lingkai Kong; Yuanqi Du; Wenhao Mu; Kirill Neklyudov; Valentin De Bortoli; Dongxia Wu; Haorui Wang; Aaron M. Ferber; Yian Ma; Carla P. Gomes; Chao Zhang 014 (AISTATS, 2025)

Learning to Explore and Exploit with GNNs for Unsupervised Combinatorial Optimization. - Utku Umur Acikalin; Aaron M. Ferber; Carla P. Gomes (ICLR, 2025)