arXiv Machine Learning
Aug 3
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).
Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization
The paper introduces an active preference learning framework for many-objective Bayesian optimization that models preferences as a Dirichlet-process mixture of latent archetypes. It uses mixture-aware information-theoretic query strategies to separately identify archetypes and refine preferences within each archetype, employing a hybrid acquisition policy. Experiments on synthetic benchmarks and a real-world chemical process design case study show that this approach outperforms existing preference-based Bayesian optimization methods and recovers interpretable latent preference structures.
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arXiv Machine Learning
Jun 9
In-Context Learning for Latent Space Bayesian Optimization
arXiv:2606. 09664v1 Announce Type: new Abstract: Bayesian optimization (BO) is a central tool for sample-efficient design, and latent-space Bayesian optimization (LSBO) extends it to structured objects such as molecules and proteins.
arXiv AI
Jun 18
Bayesian Anytime Pareto Set Identification for Multi-Objective Multi-Armed Bandits
arXiv:2606. 18785v1 Announce Type: cross Abstract: Identifying Pareto optimal solutions is critical to support multi-objective decision-making.
arXiv Machine Learning
Jul 30
BayesAME: Bayesian Active Model Evaluation
arXiv:2607. 27023v1 Announce Type: new Abstract: Evaluating large generative models across benchmarks is time-consuming and computationally expensive.
Hugging Face Trending Papers
Jun 1
Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization
Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence are not necessarily calibrated to downstream objective values. This issue becomes more pronounced in multi-objective Bayesian optimization, where different objectives may require different expert knowledge and where an LLM expert can be useful for one objective but misleading for another.
arXiv Machine Learning
Jul 14
Modernizing HEBO: a robust Bayesian optimization baseline for practical heteroskedastic and non-stationary problems
arXiv:2607. 10669v1 Announce Type: new Abstract: Bayesian optimization is increasingly used to guide data-efficient experimentation in chemistry, materials science, and related laboratory settings, but its practical performance depends strongly on how well surrogate-model assumptions match the geometry and noise structure of the underlying objective.
arXiv AI
Jun 2
Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization
arXiv:2606. 01730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence are not necessarily calibrated to downstream objective values.
arXiv AI
Aug 6
Out-Of-The-Loop Multi-Fidelity Bayesian Optimization
arXiv:2608. 04113v1 Announce Type: cross Abstract: Black-box optimization is a ubiquitous problem in science and engineering, often dealing with expensive objective functions with cheaper lower-fidelity proxies available.
arXiv Machine Learning
Sep 14
GUIDE: Generative Utility Inference and Decision Engine
GUIDE is a large‑language‑model driven architecture that elicits and infers human user preferences through conversational Bayesian adaptive sampling and symbolic rule‑based learning. It extends adaptive sampling to a wide range of elicitation questions via a flexible type system and initializes domain‑specific preference models using symbolic representations of world knowledge. In simulated investment portfolio optimization, GUIDE outperforms prior methods, LLM‑only baselines, and its own ablated variants by improving cold‑start performance and reducing recommendation regret during early interactions.
arXiv Machine Learning
Jun 2
Local Preferential Bayesian Optimization
arXiv:2606. 02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function.
arXiv Machine Learning
Aug 27
Gradient-based Sample Selection for Faster Bayesian Optimization
The paper introduces Gradient-based Sample Selection Bayesian Optimization (GSSBO), a method that builds the Gaussian process surrogate on a strategically chosen subset of samples rather than the full dataset. By using gradient information to eliminate redundant points while keeping diversity and representativeness, GSSBO achieves sublinear regret bounds and reduces the cubic computational cost of standard BO. Experiments on synthetic and real-world tasks show that this approach maintains comparable optimization performance while significantly cutting GP fitting time and resource usage.
arXiv AI
Aug 28
MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
The paper introduces MCCE, a hybrid framework that combines a frozen closed‑source large language model (LLM) with a lightweight, trainable model for multi‑objective discrete optimization. By maintaining a trajectory memory and refining the small model through reinforcement learning, the two models jointly enhance global exploration and learning. Experiments on drug‑design benchmarks demonstrate that MCCE achieves state‑of‑the‑art Pareto front quality, outperforming existing baselines.