Research map / Probabilistic and scientific modeling / Bayesian optimization
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select variables with MCTS instead of selecting a fixed-size subset randomly | BO on high-dimensional synthetic functions · Hartmann and Levy | optimization performance | Helped |
| Use MCTS variable selection with TuRBO | TuRBO · Hartmann and Levy synthetic functions | optimization performance | Mixed |
| Use MCTS variable selection instead of optimizing all dimensions | BO on high-dimensional synthetic functions · Levy10_100, Levy10_300, Hartmann6_300, Hartmann6_500 | wall clock time (seconds): 3.190 -> 2.683; 4.140 -> 3.753; 4.844 -> 3.711; 5.540 -> 4.590 | Helped |
| Use MCTS variable partitioning instead of random variable selection | variable selection · Levy10_100, Levy10_300, Hartmann6_300, Hartmann6_500 | recall: 0.100 -> 0.429; 0.030 -> 0.433; 0.020 -> 0.352; 0.012 -> 0.350 | Helped |
| Use MCTS-VS-BO rather than competing BO methods | high-dimensional synthetic optimization · synthetic functions | optimization performance | Mixed |
| Use MCTS-VS-BO rather than other methods | BO on NAS-Bench tasks · NAS-Bench-101 and NAS-Bench-201 | performance versus wall clock time | Helped |
| Combine MCTS variable selection with TuRBO | TuRBO on Hopper · Hopper | accumulative reward | Helped |
| Use MCTS-VS-BO rather than other methods | BO on Walker2d · Walker2d | accumulative reward | Helped |
| Use the best-k fill-in strategy for unoptimized variables (ablation) | MCTS-VS · high-dimensional BO | optimization performance | Helped |
| Optimize only selected variables rather than all dimensions | general variable selection for BO · high-dimensional BO | computational complexity | Helped |
Closest papers by meaning
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- BOIDS: High-Dimensional Bayesian Optimization via Incumbent-Guided Direction Lines and Subspace Embeddings (AAAI 2025 · similarity 0.86)
- BARK: A Fully Bayesian Tree Kernel for Black-box Optimization (ICML 2025 · similarity 0.86)
- Minimizing UCB: a Better Local Search Strategy in Local Bayesian Optimization (NeurIPS 2024 · similarity 0.85)
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