Summary of A Representation-learning Game For Classes Of Prediction Tasks, by Neria Uzan and Nir Weinberger
A representation-learning game for classes of prediction tasks
by Neria Uzan, Nir Weinberger
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 The proposed game-based formulation for learning dimensionality-reducing representations of feature vectors leverages prior knowledge on future prediction tasks. In this adversarial game, the first player selects a representation, while the second player chooses a prediction task from a given class. The goal is to minimize regret by optimizing the representation under mean squared error loss. Theoretical results show the effectiveness of prior knowledge and the usefulness of randomizing representations. An efficient algorithm is proposed for general cases, requiring only gradients of the loss function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to learn better feature vectors using information about future tasks. It’s like a game where one player chooses a feature vector, and the other player picks a task to predict. The goal is to make predictions as accurate as possible while considering what we know about future tasks. The results show that using this prior knowledge helps improve the quality of the feature vectors. A simple algorithm is also proposed to make it easy to implement. |
Keywords
* Artificial intelligence * Loss function
