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].
Linear Streaming Bandit: Regret Minimization and Fixed-Budget Epsilon-Best Arm Identification
Authors: Yuming Shao, Zhixuan Fang
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of all proposed algorithms through experiments on both synthetic and real-world datasets. ... We implement our algorithms on both synthetic and real-world datasets. ... Their regret curves, averaged over N = 20 repetitions, are shown in Figure 2. ... The Kaggle dataset (Chaudhari 2023) contains information on more than 17k anonymous workers... The regret curves are shown in Figure 3. |
| Researcher Affiliation | Academia | 1IIIS, Tsinghua University, Beijing, China 2Shanghai Qi Zhi Institute, Shanghai, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Confidence Radius-directed Multi Pass Sampling (CR-MPS) Algorithm 2: ϵ-Grid Multi-Pass Successive Elimination (G-MP-SE) Algorithm 3: Sample Budget Assignment (SBA) Algorithm 4: Active Arm Counter Update (C-Update) Algorithm 5: Single-Pass Comparison (SPC) |
| Open Source Code | No | The paper mentions implementing algorithms and showing experimental results, but it does not provide any specific links to code repositories or explicit statements about code release. |
| Open Datasets | Yes | Real-World Dataset. The Kaggle dataset (Chaudhari 2023) contains information on more than 17k anonymous workers, including their resume details and performance scores. We select K = 10k of them and run these algorithms again on this dataset. The regret curves are shown in Figure 3. 1https://www.kaggle.com/datasets/sanjanchaudhari/employeesperformance-for-hr-analytics/data |
| Dataset Splits | No | The paper mentions using synthetic and real-world datasets and running 20 repetitions for the synthetic dataset, but it does not specify any train/test/validation splits for either dataset. |
| Hardware Specification | No | The paper mentions implementing algorithms and running experiments but provides no specific details about the hardware (e.g., CPU, GPU models, memory) used for these experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies (e.g., libraries, frameworks) or their version numbers used for the implementation or experiments. |
| Experiment Setup | No | The paper describes the parameters of the proposed algorithms (e.g., confidence parameter, regularization parameter, precision parameters, sample budget, number of passes). However, it does not provide specific hyperparameter values for a model or system, such as learning rates, batch sizes, or optimizer settings, nor other typical experimental setup details like model initialization or training schedules. |