Bibliography (19):

1. Who Models the Models That Model Models? An Exploration of GPT-3’s In-Context Model Fitting Ability

2. TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data

3. GPT-3: Language Models are Few-Shot Learners

4. https://github.com/dtsip/in-context-learning

5. Attention Is All You Need

6. In-Context Learning and Induction Heads

7. XGBoost: A Scalable Tree Boosting System

8. Decision tree heuristics can fail, even in the smoothed setting

9. ID3 Learns Juntas for Smoothed Product Distributions

10. Investigating the Limitations of the Transformers with Simple Arithmetic Tasks

11. RASP: Thinking Like Transformers

12. An Explanation of In-context Learning as Implicit Bayesian Inference

13. Wikipedia Bibliography:

    1. https://en.wikipedia.org/wiki/Linear_function  :

       https://en.wikipedia.org/wiki/Linear_function

    2. https://en.wikipedia.org/wiki/Least_squares  :

       https://en.wikipedia.org/wiki/Least_squares

    3. Lasso (statistics)

    4. Rectifier (neural networks)

    5. Decision tree

    6. Decision tree learning

    7. XGBoost