Summary of A Directional Rockafellar-uryasev Regression, by Alberto Arletti
A Directional Rockafellar-Uryasev Regression
by Alberto Arletti
First submitted to arxiv on: 4 Nov 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 Most researchers struggle with biased datasets, which can lead to unreliable estimates. For example, Twitter training data often differs significantly from testing offline data due to selection bias. To address this issue, several methods have been developed, such as distributionally robust optimization (DRO) and learning fairness. One potential solution is to leverage meta-information, which researchers may possess about the form and extent of selection bias in their dataset. A novel loss function that incorporates two types of meta-data information – quantity and direction (under or over sampling) of bias – is proposed. This loss function is then implemented through a neural network, specifically the directional Rockafellar-Uryasev (dRU) regression model. The dRU model is tested on a biased training dataset, a Big Data online drawn electoral poll. Results show that including meta-information improves electoral results predictions compared to a model without it. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Big data often has selection bias, which means the training and testing datasets are different. This can make it hard to get accurate estimates. One way to solve this problem is by using extra information about the dataset’s biases. Researchers who have worked with these datasets before might know things like how much the bias affects the estimates or in what direction (too high or too low). I propose a new loss function that uses this extra information. This loss function is part of a neural network, specifically a directional Rockafellar-Uryasev regression model. We tested this model on an electoral poll dataset and found that including the extra information makes our predictions more accurate. |
Keywords
» Artificial intelligence » Loss function » Neural network » Optimization » Regression
