Research map / Probabilistic and scientific modeling
Bayesian optimization: research map
397 accepted papers on Bayesian optimization in Probabilistic and scientific modeling, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 7 approaches. The busiest year so far is 2026.
Within Probabilistic and scientific modeling, its share held steady from 4.5% in 2023–24 to 4.4% in 2025–26 (92 → 134 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
acquisition · bayesian · multi objective · 307 papers
Approaches in this cluster:
- Acquisition function design (97 papers)
Develop new acquisition criteria and stopping rules for sequential Bayesian optimization. - Local and high-dimensional Bayesian optimization (90 papers)
Make Bayesian optimization scale with local search, structure, and analyses of high-dimensional behavior. - Multi-fidelity hyperparameter optimization (44 papers)
Tune hyperparameters efficiently by allocating budget across fidelities and early stopping. - Multi-objective Bayesian optimization (33 papers)
Approximate Pareto fronts with batch, diversity, and preference-aware acquisition. - Regret bounds for Bayesian optimization (43 papers)
Prove regret guarantees for Bayesian and preferential optimization in kernel bandits.
Most cited and most cited since 2024:
- BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning (NeurIPS 2019 · 294 citations)
- Bayesian Optimization with Robust Bayesian Neural Networks (NeurIPS 2016 · 263 citations)
- Constrained Bayesian Optimization under Partial Observations: Balanced Improvements and Provable Convergence (AAAI 2024 · 15 citations)
- Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization (AAAI 2024 · 11 citations)
black box · box optimization · offline · 90 papers
Approaches in this cluster:
- Offline black-box optimization with surrogates (56 papers)
Optimize designs from static datasets using learned surrogates, generative models, and language-model embeddings. - High-dimensional Bayesian optimization (34 papers)
Scale Bayesian optimization to high-dimensional and discrete spaces using structured kernels and search-space learning.
Most cited and most cited since 2024:
- Multi-fidelity Bayesian Optimisation with Continuous Approximations (ICML 2017 · 104 citations)
- Scalable Hyperparameter Transfer Learning (NeurIPS 2018 · 77 citations)
- A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences (NeurIPS 2024 · 3 citations)
- Grey-Box Bayesian Optimization for Sensor Placement in Assisted Living Environments (AAAI 2024 · 3 citations)
Related topics in Probabilistic and scientific modeling
- Dynamical systems (969)
- Uncertainty and Gaussian processes (1,162)
- Molecules and proteins (1,990)
- Variational and generative sampling (2,060)
- Treatment effects (421)
- Differential equations and PDEs (614)
- Causal discovery (830)