Learning in High Dimension Always Amounts to Extrapolation

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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High-Dimensional Datasets? You're Probably Extrapolating (and That's Okay)

This paper argues that in high-dimensional data (like images), machine learning models almost always extrapolate rather than interpolate, meaning they make predictions for data points outside the range of their training data. Surprisingly, the authors find that this extrapolation doesn't necessarily hurt performance and might even be crucial for the success of current models.

Possible Conflicts of Interest

Authors are employed by Facebook AI Research, which has a vested interest in advancing machine learning techniques.

Identified Weaknesses

Limited real-world application examples

While the theoretical arguments are compelling, the paper would benefit from more diverse, real-world applications showcasing the implications of extrapolation. For instance, demonstrating how extrapolation affects model robustness to adversarial attacks or distribution shifts would strengthen the paper's practical relevance.

Oversimplification of "interpolation regime"

The paper equates "interpolation regime" with zero training loss, potentially neglecting nuances in model behavior. A model might achieve zero training loss but still exhibit extrapolative behavior in certain regions of the data space.

Rating Explanation

Strong theoretical and empirical evidence challenging common assumptions about interpolation in high-dimensional data. The limited practical demonstrations and potential oversimplification of "interpolation regime" prevent a top rating.

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