Improving FMQA via Initial Training Data Design Considering Marginal Bit Coverage in One-Hot Encoding
Factorization machine with quadratic-optimization annealing (FMQA) is a black-box optimization method that combines a factorization machine (FM) surrogate with QUBO-based search by an Ising machine. When FMQA is applied to integer or discretized continuous variables via one-hot encoding, uniform random initial sampling can leave many binary variables never active in the initial training data, and the corresponding FM parameters receive no direct gradient updates from the observed responses. We address this by designing the initial training data to achieve complete marginal bit coverage, namely, ensuring that every binary variable obtained by one-hot encoding takes the value one at least o...
| Authors | Taiga Hayashi, Yuya Seki, Kotaro Terada et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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