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Aaron M. Ferber
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2020 – today
- 2025
- [c16]Marc Grimson, Zhongdi Qu, Yue Mao, Aaron M. Ferber, Felipe Siqueira Pacheco, Sebastian Heilpern, Hector Angarita, Alexander Flecker, Carla P. Gomes:
Constraint-aware Pareto Optimization for Tree-Structured Networks: Addressing Decarbonization Targets with Hydropower Expansion. AAAI 2025: 28015-28023 - 2024
- [c15]Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron M. Ferber, Louis-Martin Rousseau:
Learning Lagrangian Multipliers for the Travelling Salesman Problem. CP 2024: 22:1-22:18 - [c14]Aaron M. Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian:
GenCO: Generating Diverse Designs with Combinatorial Constraints. ICML 2024 - [c13]Taoan Huang, Aaron M. Ferber, Arman Zharmagambetov, Yuandong Tian, Bistra Dilkina:
Contrastive Predict-and-Search for Mixed Integer Linear Programs. ICML 2024 - [c12]Brendan Hogan Rappazzo, Aaron M. Ferber, Carla Gomes:
Critic Loss for Image Classification. ICMLA 2024: 719-726 - [c11]Brendan Hogan Rappazzo, Yingheng Wang, Aaron M. Ferber, Carla Gomes:
GEM-RAG: Graphical Eigen Memories for Retrieval Augmented Generation. ICMLA 2024: 1259-1264 - [i14]Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron M. Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang:
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints. CoRR abs/2402.18012 (2024) - [i13]Weimin Huang, Taoan Huang, Aaron M. Ferber, Bistra Dilkina:
Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods. CoRR abs/2406.06954 (2024) - [i12]Brendan H. Rappazzo, Aaron M. Ferber, Carla P. Gomes:
Critic Loss for Image Classification. CoRR abs/2409.15565 (2024) - [i11]Brendan H. Rappazzo, Yingheng Wang, Aaron M. Ferber, Carla P. Gomes:
GEM-RAG: Graphical Eigen Memories For Retrieval Augmented Generation. CoRR abs/2409.15566 (2024) - [i10]Brendan Hogan, Anmol Kabra, Felipe Siqueira Pacheco, Laura Greenstreet, Joshua Fan, Aaron M. Ferber, Marta Ummus, Alecsander Brito, Olivia J. Graham, Lillian R. Aoki, Drew Harvell, Alex Flecker, Carla P. Gomes:
AiSciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification. CoRR abs/2410.21480 (2024) - 2023
- [j2]Alice Paul
, Daniel Freund, Aaron M. Ferber, David B. Shmoys, David P. Williamson:
Erratum to "Budgeted Prize-Collecting Traveling Salesman and Minimum Spanning Tree Problems". Math. Oper. Res. 48(4): 2304-2307 (2023) - [c10]Taoan Huang
Local Branching Relaxation Heuristics for Integer Linear Programs. CPAIOR 2023: 96-113 - [c9]Aaron M. Ferber, Emily Griffin, Bistra Dilkina, Burcu B. Keskin, Meredith Gore
Predicting Wildlife Trafficking Routes with Differentiable Shortest Paths. CPAIOR 2023: 460-476 - [c8]Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. ICML 2023: 10034-10052 - [c7]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. ICML 2023: 13869-13890 - [c6]Arman Zharmagambetov, Brandon Amos, Aaron M. Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian:
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. NeurIPS 2023 - [i9]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. CoRR abs/2302.01578 (2023) - [i8]Arman Zharmagambetov, Brandon Amos, Aaron M. Ferber, Taoan Huang, Bistra Dilkina, Yuandong Tian:
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. CoRR abs/2307.08964 (2023) - [i7]Aaron M. Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian:
GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature. CoRR abs/2310.02442 (2023) - [i6]Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron M. Ferber, Louis-Martin Rousseau:
Learning Lagrangian Multipliers for the Travelling Salesman Problem. CoRR abs/2312.14836 (2023) - 2022
- [c5]Aaron M. Ferber
Learning Pseudo-Backdoors for Mixed Integer Programs. CPAIOR 2022: 91-102 - [i5]Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems. CoRR abs/2210.12547 (2022) - [i4]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Local Branching Relaxation Heuristics for Integer Linear Programs. CoRR abs/2212.08183 (2022) - 2021
- [c4]Umang Gupta, Aaron M. Ferber, Bistra Dilkina, Greg Ver Steeg:
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. AAAI 2021: 7610-7619 - [c3]Aaron M. Ferber, Jialin Song, Bistra Dilkina, Yisong Yue:
Learning Pseudo-Backdoors for Mixed Integer Programs. SOCS 2021: 170-172 - [i3]Umang Gupta, Aaron M. Ferber, Bistra Dilkina, Greg Ver Steeg:
Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation. CoRR abs/2101.04108 (2021) - [i2]Aaron M. Ferber, Jialin Song, Bistra Dilkina, Yisong Yue:
Learning Pseudo-Backdoors for Mixed Integer Programs. CoRR abs/2106.05080 (2021) - 2020
- [j1]Alice Paul, Daniel Freund
Budgeted Prize-Collecting Traveling Salesman and Minimum Spanning Tree Problems. Math. Oper. Res. 45(2): 576-590 (2020) - [c2]Aaron M. Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe:
MIPaaL: Mixed Integer Program as a Layer. AAAI 2020: 1504-1511
2010 – 2019
- 2019
- [i1]Aaron M. Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe:
MIPaaL: Mixed Integer Program as a Layer. CoRR abs/1907.05912 (2019) - 2017
- [c1]Alice Paul, Daniel Freund
Prize-Collecting TSP with a Budget Constraint. ESA 2017: 62:1-62:14
Coauthor Index
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