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..
Bayesian Optimization-Based Combinatorial Assignment
Authors: Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run computational experiments in several spectrum auction domains to evaluate BOCA s performance. Our results show that BOCA achieves higher allocative ef๏ฌciency than state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1University of Zurich 2ETH Zurich 3ETH AI Center |
| Pseudocode | No | The information is insufficient. The paper describes various algorithms and methods but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks, nor does it present structured code-like steps for a procedure. |
| Open Source Code | Yes | Our source code is publicly available on Git Hub via: https://github.com/marketdesignresearch/BOCA. |
| Open Datasets | Yes | To generate synthetic CA instances, we use the following three domains from the spectrum auction test suite (SATS) (Weiss, Lubin, and Seuken 2017): LSVM, SRVM, and MRVM (see Appendix G.1 for details). |
| Dataset Splits | No | The information is insufficient. The paper mentions training on 'Dtrain' and evaluating on a 'disjoint test set Dtest' but does not provide specific split percentages or counts for training, validation, and test sets to reproduce the data partitioning. |
| Hardware Specification | No | The information is insufficient. The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The information is insufficient. While the paper mentions using a MILP solver (e.g., implied for solving Equation 10 and Theorem 2), it does not specify any software dependencies with version numbers (e.g., 'CPLEX 12.4' or 'PyTorch 1.9'). |
| Experiment Setup | Yes | To enable a fair comparison against prior work, for each domain, we use Qinit = 40 initial random queries (including the full bundle for the calculation of M100%-u UB i ) and set the query budget to Qmax = 100... We use random search (RS) (Bergstra and Bengio 2012) to optimize the hyperparameters of the mean MVNN Mmean i and of our MVNN-based u UB Mu UB i . The HPO includes the NNarchitecture parameters, training parameters, NOMU parameters, and initialization parameters (see Section 3.2). |