Summary of Improving Distribution Alignment with Diversity-based Sampling, by Andrea Napoli et al.
Improving Distribution Alignment with Diversity-based Sampling
by Andrea Napoli, Paul White
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers address the issue of domain shifts in machine learning, where models’ performances degrade when applied to real-world data. To mitigate this problem, they focus on distribution alignment methods that minimize discrepancies between distributions. However, these estimates can be noisy when trained using stochastic gradient descent (SGD) and may lead to domain misalignments if not managed properly. To overcome this limitation, the authors propose inducing diversity in each sampled minibatch, which balances data and reduces variance of gradients, enhancing generalization ability. The study presents two options for diversity-based data samplers: k-determinantal point process (k-DPP) and k-means++ algorithm, serving as drop-in replacements for standard random samplers. In a real-world bioacoustic event detection task, both approaches demonstrate improved representation of the full dataset, reduced distance estimation error between distributions, and enhanced out-of-distribution accuracy for two distribution alignment algorithms and standard ERM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models often struggle when applied to real-world data because they’re not prepared for changes in what they learn. This is called a domain shift. To help with this problem, researchers are working on methods that can learn features that work the same way no matter where they come from. These methods try to get rid of any differences between the training and test data. However, when you use computers to train these models, you don’t always get accurate results because the computer is making random choices about what data to look at next. This can make it hard for the model to learn good features. To solve this problem, the researchers in this paper suggest letting the computer mix up the training data a little bit so that it’s not just looking at the same old things all the time. They propose two ways to do this: one way uses a special kind of math called k-DPP, and another way uses something called k-means++. These methods can replace the random way computers usually choose which data to look at next. |
Keywords
» Artificial intelligence » Alignment » Event detection » Generalization » K means » Machine learning » Stochastic gradient descent
