Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Online Corrupted User Detection and Regret Minimization
Authors: Zhiyong Wang, Jize Xie, Tong Yu, Shuai Li, John C.S. Lui
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With extensive experiments, our methods achieve superior performance over previous bandit algorithms and high corrupted user detection accuracy. |
| Researcher Affiliation | Collaboration | Zhiyong Wang The Chinese University of Hong Kong EMAIL Jize Xie Shanghai Jiao Tong University EMAIL Tong Yu Adobe Research EMAIL Shuai Li Shanghai Jiao Tong University EMAIL John C.S. Lui The Chinese University of Hong Kong EMAIL |
| Pseudocode | Yes | Algorithm 1 RCLUB-WCU |
| Open Source Code | No | The paper does not provide explicit statements or links for the open-source code of the described methodology. |
| Open Datasets | Yes | We use three real-world data Movielens [11], Amazon[31], and Yelp [33]. |
| Dataset Splits | No | The paper describes data generation and processing, but does not explicitly mention train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use u = 1, 000 users and m = 10 clusters, where each cluster contains 100 users. We randomly select 100 users as the corrupted users. The preference and arm (item) vectors are drawn in d 1 (d = 50) dimensions with each entry a standard Gaussian variable and then normalized, added one more dimension with constant 1, and divided by 2 [21]. We fix an arm set with |A| = 1000 items, at each round, 20 items are randomly selected to form a set At to choose from. Following [40, 3], in the first k rounds, we always flip the reward of corrupted users by setting rt = x T atฮธit,t + ฮทt. And we leave the remaining T k rounds intact. Here we set T = 1, 000, 000 and k = 20, 000. |