Joint Entropy Search for Multi-Objective Bayesian Optimization
Authors: Ben Tu, Axel Gandy, Nikolas Kantas, Behrang Shafei
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate the JES acquisition function on a range of synthetic and real-world benchmark problems. We compare this approach with some popular acquisition functions in multi-objective BO: TSEMO [12], Par EGO [51], NPar EGO [19], EHVI [18], NEHVI [19], PES [31, 33] and MES-0 [80]. We present the log HV discrepancy results for both the sequential and batch experiments in Figure 5. |
| Researcher Affiliation | Collaboration | Ben Tu Axel Gandy Nikolas Kantas Behrang Shafei Imperial College London BASF SE ben.tu16@imperial.ac.uk |
| Pseudocode | Yes | Algorithm 1: Joint Entropy Search (JES). |
| Open Source Code | Yes | The complete details of the experiments are outlined in Appendix L, whilst the code is available at https://github.com/benmltu/JES. |
| Open Datasets | Yes | Synthetic benchmark. We consider the ZDT2 [22] benchmark with D = 6 inputs and M = 2 objectives. Chemical reaction. This benchmark considers a nucleophilic aromatic substitution reaction (Sn Ar) between 2,4-difluoronitrobenzene and pyrrolidine in ethanol to produce a mixture of a desired product and two side-products [45]. Pharmaceutical manufacturing. This problem is concerned with optimizing the Penicillin production process outlined in [56]. Marine design. This problem considers optimizing a family of bulk carriers subject to the constraints imposed for ships travelling through the Panama Canal [65, 73]. We consider the reformulation in [83], which converts the constraints into another objective. |
| Dataset Splits | No | The problem context is Bayesian Optimization, which involves sequential data acquisition rather than pre-defined dataset splits for training, validation, and testing. The paper does not specify fixed percentages or counts for these splits. |
| Hardware Specification | Yes | Wall times are reported in seconds and are measured on a MacBook Pro M1 Max. |
| Software Dependencies | No | All algorithms are based on the open source Python library Bo Torch [3], which uses features from GPy Torch [30] for Gaussian process regression and Py Torch [66] for automatic differentiation. All experiments are repeated using 100 different initial seeds and we generate the Pareto set recommendation ˆX of 50 points by maximizing the posterior mean using a multi-objective solver (NSGA2 [22] from the Pymoo library [10]). The paper mentions these software packages but does not provide specific version numbers for them. |
| Experiment Setup | Yes | All experiments are repeated using 100 different initial seeds and we generate the Pareto set recommendation ˆX of 50 points by maximizing the posterior mean using a multi-objective solver (NSGA2 [22] from the Pymoo library [10]). We corrupt the observations with additive Gaussian noise with zero-mean and standard deviation set to approximately 10% of the objective ranges. |