Summary of Ferero: a Flexible Framework For Preference-guided Multi-objective Learning, by Lisha Chen et al.
FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
by Lisha Chen, AFM Saif, Yanning Shen, Tianyi Chen
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel framework, FERERO (Flexible framEwork for pREfeRence-guided multi-Objective learning), is proposed to find specific preference-guided Pareto solutions in multi-objective problems. This approach casts the problem as a constrained vector optimization issue, incorporating two types of preferences: relative and absolute. Convergent algorithms are developed with single-loop and stochastic variants, which adaptively adjust to constraint and objective values, eliminating the need for subproblems. Experimental results on multiple benchmarks demonstrate FERERO’s competitiveness in finding preference-guided optimal solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new way to find the best balance between different goals when there are many conflicting objectives. This is called multi-objective learning. The authors make it possible to define what makes one option better than another using two types of preferences: how things relate to each other and specific constraints. They also develop algorithms to solve this problem, which can adapt to changing requirements. The results show that their method is very effective in finding the best solutions. |
Keywords
* Artificial intelligence * Optimization
