3 papers
cs.LG2026
Exact Subgraph Isomorphism Network with Mixed Norm Constraint for Predictive Graph Mining
Taiga Kojima, Haruto Kajita, Ayato Kohara +1
In the graph-level prediction task (predict a label for a given graph), the information contained in subgraphs of the input graph plays a key role. In this paper, we propose Exact…
stat.ML2025
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Bayesian optimization is a powerful tool for optimizing an expensive-to-evaluate black-box function. In particular, the effectiveness of expected improvement (EI) has been demonstr…
cs.LG2025
Regret Analysis for Randomized Gaussian Process Upper Confidence Bound
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically established algorithm for Bayesian optimization (BO), where we assume the objective function follows a GP. O…