Summary of Jacobian Descent For Multi-objective Optimization, by Pierre Quinton et al.
Jacobian Descent for Multi-Objective Optimization
by Pierre Quinton, Valérian Rey
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces Jacobian descent (JD), a new optimization algorithm that can handle multiple conflicting objectives by iteratively updating parameters using the Jacobian matrix of a vector-valued objective function. Unlike existing methods, JD resolves conflicts between objectives while preserving an influence proportional to their norm, providing stronger convergence guarantees and enabling instance-wise risk minimization (IWRM) for novel learning paradigms. The authors demonstrate the effectiveness of JD in simple image classification tasks, achieving promising results compared to direct loss minimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to solve problems where you need to balance multiple things that are important but different. It’s like trying to get as close to zero on a seesaw with two weights, one representing accuracy and the other representing time. The authors came up with an algorithm called Jacobian descent (JD) that can handle this type of problem by looking at how each part changes if you make small adjustments. They show that JD is better than what’s currently being used and can even solve problems where each piece of data is important in its own way. |
Keywords
» Artificial intelligence » Image classification » Objective function » Optimization
