Summary of Pmgda: a Preference-based Multiple Gradient Descent Algorithm, by Xiaoyuan Zhang and Xi Lin and Qingfu Zhang
PMGDA: A Preference-based Multiple Gradient Descent Algorithm
by Xiaoyuan Zhang, Xi Lin, Qingfu Zhang
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 In this paper, researchers tackle a critical problem in machine learning where multiple objectives conflict. They propose a novel framework to find a Pareto solution that matches a decision maker’s preference. The framework uses a constraint function to align the solution with the user’s preference, which can be optimized simultaneously with multiple objective functions. This method is tested on standard multiobjective benchmarks and real-world problems with thousands of variables. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best solution when we have many things we want to achieve at the same time. The researchers created a new way to solve this problem by making sure their answer fits what someone wants. They did this by adding a special rule to their search process that makes sure it matches the person’s preferences. This method works well on big problems with lots of variables. |
Keywords
* Artificial intelligence * Machine learning
