Research map / Probabilistic and scientific modeling / Bayesian optimization
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use composite posterior-scoring methods (IPS-A/IPS-B) instead of other evaluated approaches | Biological sequence design · UTR and AMP | area under Top-100 curve | Helped |
| Use forward-scoring methods (IS-A/IS-B) instead of composite methods | Biological sequence design · TfBind | top-sequence scores | Mixed |
| Use composite methods or DbAS rather than the other evaluated methods | Biological sequence design · Fluo | area under Top-100 curve | Helped |
Closest papers by meaning
- A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences (NeurIPS 2024 · similarity 0.89)
- Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks (ICML 2025 · similarity 0.87)
- Variational Search Distributions (ICLR 2025 · similarity 0.85)
- Improved Off-policy Reinforcement Learning in Biological Sequence Design (ICML 2025 · similarity 0.85)
- Designing Biological Sequences without Prior Knowledge Using Evolutionary Reinforcement Learning (AAAI 2024 · similarity 0.85)
- Kernel-Based Evaluation of Conditional Biological Sequence Models (ICML 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.