Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Max-value Entropy Search for Multi-Objective Bayesian Optimization
Authors: Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several synthetic and real-world benchmark problems show that MESMO consistently outperforms the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Syrine Belakaria, Aryan Deshwal, Janardhan Rao Doppa School of EECS, Washington State University EMAIL |
| Pseudocode | Yes | Algorithm 1 MESMO Algorithm |
| Open Source Code | No | The paper mentions using and referencing code for baselines ('Spearmint', 'Py GMO library') but does not provide an explicit statement or link for the open-sourcing of their own MESMO implementation. |
| Open Datasets | Yes | We optimize a dense neural network over the MNIST dataset [13]. (...) We employed four real-world benchmarks with data available at [31, 21]. (...) We also employ two benchmarks from the general multi-objective optimization literature [16, 4]. |
| Dataset Splits | Yes | We employ 10K instances for validation and 50K instances for training. |
| Hardware Specification | Yes | We run all experiments on a machine with the following configuration: Intel i7-7700K CPU @ 4.20GHz with 8 cores and 32 GB memory. |
| Software Dependencies | No | The paper mentions software by name ('Spearmint', 'Py GMO library') and provides links, but does not specify version numbers for these or other software components (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | The hyper-parameters are estimated after every 5 function evaluations. We initialize the GP models for all functions by sampling initial points at random from a Sobol grid. (...) We train the network for 100 epochs for evaluating each candidate hyper-parameter values on validation set. |