Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Order Simple Regret for Gaussian Process Bandits
Authors: Sattar Vakili, Nacime Bouziani, Sepehr Jalali, Alberto Bernacchia, Da-shan Shiu
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,908 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,447 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide numerical experiments on the simple regret performance of MVR comparing it to GP-UCB [19, 20], GP-PI [26] and GP-EI [26]. |
| Researcher Affiliation | Collaboration | Media Tek Research EMAIL +Imperial College London EMAIL |
| Pseudocode | Yes | Algorithm 1 Maximum Variance Reduction (MVR) 1: Initialization: k, X, f, σ2 0(x) k(x, x). 2: for n = 1, 2, . . . , N do 3: xn = argmaxx X σ2 n 1(x), where a tie is broken arbitrarily. 4: Update σ2 n(.) according to (2). 5: end for 6: Update µN(.) according to (2). 7: return ˆx N = argmaxx X µN(x), where a tie is broken arbitrarily. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for its methodology. |
| Open Datasets | No | We follow the experiment set up in [20] to generate test functions from the RKHS. First, 100 points are uniformly sampled from interval [0, 1]. A GP sample with kernel k is drawn over these points. Given this sample, the mean of posterior distribution is used as the test function f. (The paper generates synthetic data, rather than using a publicly available dataset with concrete access information). |
| Dataset Splits | No | The paper describes generating synthetic data and testing, but does not provide specific train/validation/test dataset splits needed for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions algorithms like GP-UCB, GP-PI, GP-EI, but does not provide specific version numbers for any software or libraries used in the experiments. |
| Experiment Setup | Yes | Parameter λ2 is set to 1% of the function range. For IGP-UCB we set the parameters exactly as described in [20]. The GP model is equipped with SE or Matérn-2.5 kernel with l = 0.2. We use 2 different models for the noise: a zero mean Gaussian with variance equal to λ2 (a sub-Gaussian distribution) and a zero mean Laplace with scale parameter equal to λ (a light-tailed distribution). We run each experiment over 25 independent trials. |