Summary of Solving Hidden Monotone Variational Inequalities with Surrogate Losses, by Ryan D’orazio et al.
Solving Hidden Monotone Variational Inequalities with Surrogate Losses
by Ryan D’Orazio, Danilo Vucetic, Zichu Liu, Junhyung Lyle Kim, Ioannis Mitliagkas, Gauthier Gidel
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 The proposed surrogate-based approach is a principled method for solving variational inequality (VI) problems using deep learning. By leveraging assumptions about hidden monotone structure, interpolation, and sufficient optimization of surrogates, the approach guarantees convergence while providing a unifying perspective on existing methods. The method is compatible with existing deep learning optimizers like ADAM and demonstrates effectiveness in min-max optimization and minimizing projected Bellman error. Additionally, a novel variant of TD(0) is proposed for deep reinforcement learning, which is more compute and sample efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to solve complex problems using deep learning. It shows that by making some assumptions about the problem, we can use existing deep learning tools to find solutions. This approach is useful because it guarantees that the solution will be correct and provides a clear understanding of how different methods work together. The authors also tested their method on specific problems and found that it works well. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Reinforcement learning
