Multi-Feedback Bandit Learning with Probabilistic Contexts
Authors: Luting Yang, Jianyi Yang, Shaolei Ren
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulation on machine learning model recommendation further validates the sub-linearity of our cumulative regret and demonstrates that our algorithm outperforms the approach that selects arms based on the most probable context. |
| Researcher Affiliation | Academia | Luting Yang , Jianyi Yang and Shaolei Ren University of California, Riverside {lyang029, jyang239, shaolei}@ucr.edu |
| Pseudocode | Yes | Algorithm 1 Multi-Feedback Probabilistic Contextual UCB |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, nor does it include links to repositories or mention code in supplementary materials. |
| Open Datasets | No | For evaluation purposes, we run experiments and collect measured data of five image classification DNN models from Tensor Flow Hub running on two cellphones (Vivo V1838A and Google Pixel 3a) and two tablets (Samsung Galaxy Tab A7 and Vankyo Matrix Pad Z4). |
| Dataset Splits | No | The paper mentions collecting its own measured data and generating probabilities for context bundles, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits) for reproducibility. |
| Hardware Specification | No | The paper mentions the mobile devices (Vivo V1838A, Google Pixel 3a, Samsung Galaxy Tab A7, Vankyo Matrix Pad Z4) used for collecting DNN model data, but it does not specify the hardware (e.g., CPU, GPU models, memory) used to run the bandit learning algorithm simulations. |
| Software Dependencies | No | The paper mentions using Tensor Flow Hub and a radial basis function kernel but does not provide specific version numbers for any software dependencies or libraries required for replication. |
| Experiment Setup | No | The paper mentions generating probabilistic contexts and random utility functions for simulation, and uses a radial basis function kernel, but it does not provide specific hyperparameter values (e.g., values for λ or β) or detailed training configurations for its experiments. |