Summary of Guided Learning: Lubricating End-to-end Modeling For Multi-stage Decision-making, by Jian Guo et al.
Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making
by Jian Guo, Saizhuo Wang, Yiyan Qi
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP)
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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 the challenge of training neural networks for multi-stage decision-making applications like recommendation systems, autonomous driving, and quantitative investment strategies. The problem is that current end-to-end models face difficulties in optimizing the entire process, leading to suboptimal or even collapsed solutions. To overcome these issues, the authors propose a novel framework called Guided Learning, which introduces a “guide” function to direct gradients away from suboptimal collapse. This approach is particularly useful for scenarios lacking explicit supervisory labels. The paper also explores connections between Guided Learning and classic machine learning paradigms like supervised, unsupervised, semi-supervised, multi-task, and reinforcement learning. Experimental results demonstrate that Guided Learning outperforms both traditional stage-wise approaches and existing end-to-end methods in building quantitative investment strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines make better decisions by training them to think step-by-step, rather than all at once. Imagine you’re trying to decide what stocks to invest in – this is a multi-stage process that involves finding good investments, predicting how well they’ll do, and then choosing which ones to buy or sell. The problem is that current methods can get stuck or make bad choices if they don’t have enough information. To solve this, the researchers created a new way of training machines called Guided Learning, which helps them focus on each step in the decision-making process. This approach works well even when there’s no clear right answer. The results show that this method can help create better investment strategies than other methods. |
Keywords
* Artificial intelligence * Machine learning * Multi task * Reinforcement learning * Semi supervised * Supervised * Unsupervised
