High-dimensional Experimental Design and Kernel Bandits
Authors: Romain Camilleri, Kevin Jamieson, Julian Katz-Samuels
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 2.5. Empirical evaluation of allocation methods We briefly describe illustrative experiments (see the supplementary material for more details). G-optimal design experiment: ... Figure 2 depicts the results... G-optimal design in an RKHS: ... Figure 3 depicts the results... RIPS vs. IPS: ... Figure 4 shows that as m grows, the performance of IPS degrades relative to RIPS... |
| Researcher Affiliation | Academia | Romain Camilleri 1 Julian Katz-Samuels 2 Kevin Jamieson 1 1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 2University of Wisconsin, Madison, WI. |
| Pseudocode | Yes | Algorithm 1 RIPS for Experimental Designs in an RKHS Input: Finite sets X Rd and V H, feature map φ : Rd H, number of samples τ, regularization γ > 0, robust mean estimator bµ : R R ... Algorithm 2 PTR for Experimental Designs in an RKHS ... Algorithm 3 RIPS for Regret Minimization ... Algorithm 4 RIPS for Pure Exploration |
| Open Source Code | No | The paper does not include any explicit statement or link providing access to open-source code for the described methodology. |
| Open Datasets | No | The paper describes how experimental data was generated ('We generate x1, . . . , xn by sampling xi N(0, Σ)...' and 'We let X = {0, ( 1/m)2, . . . , (m-1/m)2, 1}...') but does not provide concrete access information (link, DOI, formal citation) to a publicly available or open dataset. |
| Dataset Splits | No | The paper describes experimental settings like 'G-optimal design experiment' and 'G-optimal design in an RKHS' but does not specify explicit training, validation, or test dataset splits or cross-validation setup. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | No | The paper mentions parameters for the experimental setups such as 'd=50', 'm=500', 'ϕ=0.025', and 'γ=0.005' but does not provide details on specific hyperparameters like learning rates, batch sizes, or optimizer settings typically found in machine learning experimental setups. |