Contextual Bandits with Online Neural Regression
Authors: Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, our experimental results on various datasets demonstrate that our algorithms, especially the one based on KL loss, persistently outperform existing algorithms. Finally, in Section 5 we compare our algorithms against baseline algorithms for Neu CBs. |
| Researcher Affiliation | Academia | Rohan Deb, Yikun Ban, Shiliang Zhou, Jingrui He, & Arindam Banerjee University of Illinois, Urbana-Champaign {rd22,yikunb2,szuo3,jingrui,arindamb}@cs.illinois.edu |
| Pseudocode | Yes | Algorithm 1 Neural Square CB (Neu Square CB); Uses Square loss. Algorithm 2 Neural Fast CB (Neu Fast CB); Uses KL loss. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about releasing the source code for their proposed methods. A link to a baseline's code is provided, but not their own. |
| Open Datasets | Yes | We consider a collection of 6 multiclass classification datasets from the openml.org platform: covertype, fashion, Magic Telescope, mushroom, Plants and shuttle. |
| Dataset Splits | No | The paper mentions datasets and a 'standard evaluation strategy' but does not provide specific details on train/validation/test splits, percentages, or sample counts for the datasets used in their experiments. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, cloud platforms, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions using neural networks and various models but does not specify the versions of any software libraries, frameworks (like PyTorch or TensorFlow), or programming languages used. |
| Experiment Setup | Yes | Both Neu Square CB and Neu Fast CB use a 2-layered Re Lu network with 100 hidden neurons. The last layer in Neu RIG uses a linear activation while Neu Fast CB uses a sigmoid. We perform a grid-search over the regularization parameter λ over (1, 0.1, 0.01) and the exploration parameter ν over (0.1, 0.01, 0.001). Neural Epsilon uses the same neural architecture and the exploration parameter ϵ is searched over (0.1, 0.05, 0.01). For all the algorithms we also do a grid-search for the step-size over (0.01, 0.005, 0.001). |