Summary of In-context Learning For Mixture Of Linear Regressions: Existence, Generalization and Training Dynamics, by Yanhao Jin et al.
In-context Learning for Mixture of Linear Regressions: Existence, Generalization and Training Dynamics
by Yanhao Jin, Krishnakumar Balasubramanian, Lifeng Lai
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 Transformers are capable of learning in-context capabilities for d-dimensional mixture of linear regression models, achieving a prediction error of order () with high probability. Theoretical insights include generalization bounds and training dynamics. In the high signal-to-noise ratio (SNR) regime, transformers can achieve excess risk bounds of order (L/). The paper also analyzes the training dynamics of single linear self-attention layers, demonstrating convergence to a global optimum through gradient flow optimization. Simulations show that transformers perform well on this task, potentially outperforming baselines like Expectation-Maximization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers can learn in-context skills for certain types of math problems. This means they can solve these problems even if they haven’t seen them before. The researchers showed that transformers are good at solving this type of problem and can do better than some other methods. They also looked at how transformers train to learn this skill and found that they can reach a perfect solution with the right starting point. |
Keywords
» Artificial intelligence » Generalization » Linear regression » Optimization » Probability » Self attention
