Summary of Diffusion Models As Constrained Samplers For Optimization with Unknown Constraints, by Lingkai Kong et al.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
by 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
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to address real-world optimization problems when analytic objective functions or constraints are unavailable. Specifically, it focuses on scenarios where feasibility constraints are not given explicitly, which can lead to spurious solutions that are unrealistic in practice. The authors propose performing optimization within the data manifold using diffusion models and reformulating 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. They also introduce two different sampling methods, one for differentiable objectives and another for non-differentiable objectives. The proposed approach is evaluated on synthetic and real-world datasets, achieving better or comparable performance with previous state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve big problems when we don’t have the right clues to follow. It’s like trying to find a way out of a maze without knowing where the exit is! The researchers came up with a clever solution by using “diffusion models” to find the best path. They even came up with two different ways to do it, depending on whether we know what the goal looks like or not. They tested their ideas on some pretend data and real-world problems, and it worked really well! |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Objective function * Optimization
