1 code implementation • 28 Oct 2024 • Brendan Hogan, Anmol Kabra, Felipe Siqueira Pacheco, Laura Greenstreet, Joshua Fan, Aaron Ferber, Marta Ummus, Alecsander Brito, Olivia Graham, Lillian Aoki, Drew Harvell, Alex Flecker, Carla Gomes
Trust and interpretability are crucial for the use of Artificial Intelligence (AI) in scientific research, but current models often operate as black boxes offering limited transparency and justifications for their outputs.
no code implementations • 23 Sep 2024 • Brendan Hogan Rappazzo, Aaron Ferber, Carla Gomes
CrtCl formulates image classification training in a generator-critic framework, with a base classifier acting as a generator, and a correctness critic imposing a loss on the classifier.
no code implementations • 23 Sep 2024 • Brendan Hogan Rappazzo, Yingheng Wang, Aaron Ferber, Carla Gomes
The ability to form, retrieve, and reason about memories in response to stimuli serves as the cornerstone for general intelligence - shaping entities capable of learning, adaptation, and intuitive insight.
no code implementations • 28 Feb 2024 • Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model.
no code implementations • 22 Dec 2023 • Augustin Parjadis, Quentin Cappart, Bistra Dilkina, Aaron Ferber, Louis-Martin Rousseau
Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint).
no code implementations • 3 Oct 2023 • Aaron Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian
Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images.
no code implementations • 3 Feb 2023 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large number of combinatorial optimization problems.
no code implementations • 15 Dec 2022 • Taoan Huang, Aaron Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner
LNS relies on heuristics to select neighborhoods to search in.
no code implementations • 22 Oct 2022 • Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
no code implementations • 9 Jun 2021 • Aaron Ferber, Jialin Song, Bistra Dilkina, Yisong Yue
In addition, we compare our learned approach against Gurobi, a state-of-the-art MIP solver, demonstrating that our method can be used to improve solver performance.
no code implementations • 12 Jul 2019 • Aaron Ferber, Bryan Wilder, Bistra Dilkina, Milind Tambe
It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization.