Bayesian Analysis of Combinatorial Gaussian Process Bandits
Authors: Jack Sandberg, Niklas Åkerblom, Morteza Haghir Chehreghani
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we employ our framework to address the challenging real-world problem of online energy-efficient navigation, where we demonstrate its effectiveness compared to the alternatives. [...] 4 EXPERIMENTS In this section, we consider the important real-world application of online energy-efficient navigation for electric vehicles and formulate it as a combinatorial and contextual bandit problem. [...] The cumulative regret is shown in Fig. 2. |
| Researcher Affiliation | Collaboration | Jack Sandberg1, Niklas Akerblom1,2 & Morteza Haghir Chehreghani1 1Department of Computer Science and Engineering, Chalmers University of Technology & University of Gothenburg 2Volvo Car Corporation EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Framework for combinatorial volatile semi-bandit problem [...] Algorithm 2 Compute Rectified Indices [...] Algorithm 3 SVGP Optimization Procedure |
| Open Source Code | No | The kernels, the SVGP model and Algorithm 3 was implemented using GPy Torch (Gardner et al., 2018). |
| Open Datasets | Yes | In our experiments we use the road networks of Luxembourg and Monaco (Codeca et al., 2017; Codeca & H arri, 2018, based on data by Open Street Map contributors, 2017). Elevation data (Administration de la navigation a erienne, 2018) is added to the network using QGIS and the netconvert tool from SUMO. |
| Dataset Splits | No | The six methods are evaluated 5 times each on the four routes in the Luxembourg and Monaco networks with a horizon of T = 500. The cumulative regret is shown in Fig. 2. [...] The four parametrizations are evaluated 5 times each on the four routes in the Luxembourg and Monaco networks with a horizon of T = 500. |
| Hardware Specification | No | The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. |
| Software Dependencies | No | The kernels, the SVGP model and Algorithm 3 was implemented using GPy Torch (Gardner et al., 2018). [...] Elevation data (Administration de la navigation a erienne, 2018) is added to the network using QGIS and the netconvert tool from SUMO. |
| Experiment Setup | Yes | The graph kernel is initialized with parameters νG = 2, κG = 1 and σG set according to the prior. The natural gradient descent learning rate is set to 0.1 whilst the Adam learning rate is set to 0.01. The GP model uses a batch size B of 2500 and 1 gradient step per optimization procedure. The number of inducing points is set to 1000. [...] The noise variance ς2 is set to 0.12σ2 det for all edges and the kernel lengthscales are set to 1. |