Scalable Generalized Linear Bandits: Online Computation and Hashing
Authors: Kwang-Sung Jun, Aniruddha Bhargava, Robert Nowak, Rebecca Willett
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now show our experiment results comparing GLB algorithms and hash-amenable algorithms. GLB Algorithms We compare GLOC with two different algorithms: UCB-GLM [28] and Online Learning for Logit Model (OL2M) [41].8 For each trial, we draw θ Rd and N arms (X) uniformly at random from the unit sphere. We set d = 10 and Xt = X, t 1. ... We plot the cumulative regret under the logit model in Figure 2(a). All three methods perform similarly, and we do not find any statistically significant difference based on paired t test. |
| Researcher Affiliation | Academia | Kwang-Sung Jun UW-Madison kjun@discovery.wisc.edu Aniruddha Bhargava UW-Madison aniruddha@wisc.edu Robert Nowak UW-Madison rdnowak@wisc.edu Rebecca Willett UW-Madison willett@discovery.wisc.edu |
| Pseudocode | Yes | Algorithm 1 GLOC |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('We draw θ Rd and N arms (X) uniformly at random from the unit sphere.') and does not use a publicly available or open dataset with provided access information. |
| Dataset Splits | No | The paper describes a bandit problem setup with generated data and repeated trials, but does not specify training, validation, and test dataset splits in the conventional sense for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') that would be needed for replication. |
| Experiment Setup | Yes | For each trial, we draw θ Rd and N arms (X) uniformly at random from the unit sphere. We set d = 10 and Xt = X, t 1. ... For OL2M we set the squared radius γt = c log(det(Zt)/det(Z1)), where c is a tuning parameter. For UCB-GLM, we set the radius as α = cd log t. For GLOC, we replace βONS t with c Pt s=1 g2 s||xs||2 A 1 s . While parameter tuning in practice is nontrivial, for the sake of comparison we tune c {101, 100.5, . . . , 10 3} and report the best one. We perform 40 trials up to time T = 3000 for each method and compute confidence bounds on the regret. |