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].
On Kernelized Multi-armed Bandits
Authors: Sayak Ray Chowdhury, Aditya Gopalan
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental evaluation and comparisons to existing algorithms on synthetic and real-world environments are carried out that highlight the favorable gains of the proposed strategies in many cases. |
| Researcher Affiliation | Academia | Department of Electrical Communication Engineering, Indian Institute of Science, Bengaluru, 560012, India. |
| Pseudocode | Yes | Algorithm 1 Improved-GP-UCB (IGP-UCB) and Algorithm 2 GP-Thompson-Sampling (GP-TS) are provided with structured pseudocode. |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or state that the code is publicly available. |
| Open Datasets | Yes | We use temperature data collected from 54 sensors deployed in the Intel Berkeley Research lab [...] http://db.csail.mit.edu/labdata/labdata. html. We take light sensor data collected in the CMU Intelligent Workplace in Nov 2005, which is available online as Matlab structure http://www.cs.cmu.edu/~guestrin/Class/ 10708-F08/projects/lightsensor.zip |
| Dataset Splits | No | The paper mentions running experiments for 30000 iterations over 25 independent trials, and using initial days of sensor data to 'learn algorithm parameters', and subsequent days for 'testing'. However, it does not explicitly provide specific train/validation/test splits, percentages, or cross-validation details for the datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or specific solver versions). |
| Experiment Setup | Yes | We set noise parameter R2 to be 1% of function range and use λ = R2. We used Squared Exponential kernel with lengthscale parameter l = 0.2 and Mat ern kernel with parameters ν = 2.5, l = 0.2. Parameters βt, βt, vt of IGP-UCB, GP-UCB and GP-TS are chosen as given in Section 3, with δ = 0.1, B2 = f T Kf and γt set according to theoretical upper bounds for corresponding kernels. |