Research map / Probabilistic and scientific modeling / Bayesian optimization
Local Latent Space Bayesian Optimization over Structured Inputs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use SELFIES transformer VAE instead of JTVAE (ablation) | LOL-BO · penalized logP | logP: 123.54 -> 545.17 | Helped |
| add joint training of encoder/GP (LOL-BO) to fixed-latent TuRBO (ablation) | TuRBO SELFIES · DRD3 docking, 5000 evals | docking score: -13.0 -> -15.4 | Helped |
| use local trust-region BO instead of standard global LS-BO (ablation) | LS-BO SELFIES · DRD3 docking, 5000 evals | docking score: -12.6 -> -13.0 | Helped |
| use LOL-BO instead of T-LBO | latent BO · expressions | final regret: 0.194 -> 0.079 | Helped |
| use SELFIES VAE instead of JTVAE (ablation) | latent BO · GuacaMol reconstruction | reconstruction accuracy: 0.767 -> 0.913 | Helped |
| use SELFIES VAE instead of JTVAE (ablation) | latent BO · decoding noise | decode stddev: 0.1 -> 0.04 | Helped |
| use LOL-BO instead of Graph-GA at large budget | LOL-BO SELFIES · DRD3 docking, 5000 evals | docking score: -16.5 -> -15.4 | Hurt |
Closest papers by meaning
- Inversion-based Latent Bayesian Optimization (NeurIPS 2024 · similarity 0.90)
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- Joint Composite Latent Space Bayesian Optimization (ICML 2024 · similarity 0.89)
- Latent Bayesian Optimization via Autoregressive Normalizing Flows (ICLR 2025 · similarity 0.89)
- Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces (ICML 2025 · similarity 0.89)
- Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization (ICML 2025 · similarity 0.88)
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