Summary of Stochastic Bandits with Relu Neural Networks, by Kan Xu et al.
Stochastic Bandits with ReLU Neural Networks
by Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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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 paper studies the stochastic bandit problem with ReLU neural network structure, achieving a regret guarantee of (). The authors propose an OFU-ReLU algorithm that explores randomly until reaching a linear regime and then uses a UCB-type linear bandit to balance exploration and exploitation. By exploiting the piecewise linear structure of ReLU activations and converting the problem into a linear bandit in a transformed feature space, the algorithm can achieve this guarantee. To remove dependence on model parameters, an OFU-ReLU+ algorithm is designed using a batching strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses neural networks to solve a special kind of optimization problem called the stochastic bandit problem. The goal is to find the best option among many possibilities, but you don’t know which one is the best until you try it. The researchers created a new algorithm that can make good choices quickly and with minimal mistakes. This is important because it can be used in many real-world applications, such as recommending products or choosing the best route. |
Keywords
» Artificial intelligence » Neural network » Optimization » Relu
