Summary of An Mrp Formulation For Supervised Learning: Generalized Temporal Difference Learning Models, by Yangchen Pan et al.
An MRP Formulation for Supervised Learning: Generalized Temporal Difference Learning Models
by Yangchen Pan, Junfeng Wen, Chenjun Xiao, Philip Torr
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper challenges traditional statistical learning by viewing data points as interconnected rather than independently distributed. The authors employ a Markov reward process to model data and reformulate supervised learning as an on-policy policy evaluation problem in reinforcement learning. They introduce a generalized temporal difference (TD) learning algorithm, connecting its solution to ordinary least squares (OLS). Under certain conditions, the TD’s solution is shown to be a more effective estimator than OLS. The authors also demonstrate the convergence of their generalized TD algorithms and show practical utility across various datasets for tasks like regression and image classification with deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper changes how we think about data by treating it as connected rather than separate points. The researchers use a special kind of math to model this connection and then apply it to make predictions. They compare their new method to an old one called ordinary least squares (OLS) and show that under certain conditions, their method is better. This means they can make more accurate predictions about things like what kind of picture you might see or how much something will cost. |
Keywords
» Artificial intelligence » Deep learning » Image classification » Regression » Reinforcement learning » Supervised
