Summary of A Unified Law Of Robustness For Bregman Divergence Losses, by Santanu Das et al.
A unified law of robustness for Bregman divergence losses
by Santanu Das, Jatin Batra, Piyush Srivastava
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 the realm of deep learning, overparameterized models are trained to near zero loss, nearly interpolating training data. However, this phenomenon is distinct from theoretical minimum requirements for interpolation. Research on overparameterization has shown that it’s necessary for robust interpolation in regression tasks using square loss. Building upon this work, our study generalizes these results to Bregman divergence losses, which encompass square and cross-entropy losses commonly used in classification tasks. Our proof relies on a bias-variance decomposition, allowing us to extend the robustness guarantees to broader loss functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine training a machine learning model to predict things based on some information. Sometimes, we need these models to be super good at predicting what they learned during training. A recent study showed that when our models have too many parameters compared to the amount of data, this helps them be more robust and accurate in making predictions. In this work, we’re taking a step further by applying these findings to different types of loss functions used in machine learning, like those for classification tasks. This allows us to better understand how our models can be made more reliable and accurate. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Deep learning » Machine learning » Regression
