Summary of Reducing Variance in Meta-learning Via Laplace Approximation For Regression Tasks, by Alfredo Reichlin et al.
Reducing Variance in Meta-Learning via Laplace Approximation for Regression Tasks
by Alfredo Reichlin, Gustaf Tegnér, Miguel Vasco, Hang Yin, Mårten Björkman, Danica Kragic
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 approach for reducing variance in gradient-based meta-learning is introduced, addressing the issue of sub-optimal generalization performance due to limited support data in meta-regression tasks. The paper formalizes the problem of task overlap, where ambiguous sample points belong to different tasks concurrently. A proposed method weighs each support point individually based on the variance of its posterior over parameters, using the Laplace approximation to estimate the posterior and express the variance in terms of the loss landscape’s curvature. Experimental results demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machines can learn from experience and apply what they’ve learned to new situations. It’s like learning a new skill or language, but for computers! The problem is that when there are many different things for a machine to learn, it can get confused and make mistakes. This paper solves this problem by finding a way to reduce the mistakes made when a machine is trying to apply what it has learned to new situations. |
Keywords
» Artificial intelligence » Generalization » Meta learning » Regression
