arXiv Machine Learning
Jun 10
mlr3mbo: Bayesian Optimization in R
arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.
Scalable Batch Bayesian Optimization Via Subspace Acquisition Functions
arXiv:2411. 16206v3 Announce Type: replace-cross Abstract: Extending Bayesian optimization to batch evaluation can enable the designer to make the most use of parallel computing technology.
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arXiv Machine Learning
6d ago
Trust Region Constrained Bayesian Optimization with Penalized Constraint Handling
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
arXiv Machine Learning
Jun 30
B3O: Scalable Boltzmann Batch Bayesian Optimization
arXiv:2606. 30228v1 Announce Type: new Abstract: Modern engineering workflows increasingly rely on massive parallel simulation, driving the need for scalable, large-batch Bayesian Optimization (BO).
arXiv Machine Learning
Jun 16
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
arXiv:2312. 06173v2 Announce Type: replace Abstract: Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well across diverse tasks.
arXiv Machine Learning
Jun 30
Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference
arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.
arXiv Machine Learning
Jul 7
Local Constrained Bayesian Optimization
arXiv:2603. 07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality.
arXiv Machine Learning
Jun 25
Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds
arXiv:2603. 01470v3 Announce Type: replace Abstract: We consider the optimization problem of an expensive-to-evaluate black-box function, in which we can obtain noisy function values in parallel.
arXiv Machine Learning
Jun 30
BaRA: Bayesian Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
arXiv:2606. 29184v1 Announce Type: new Abstract: While Low-rank adaptation (LoRA) enables highly efficient fine-tuning by constraining task-specific updates to fixed low-rank subspaces, this rigid design limits representational flexibility and often results in overconfident predictions and miscalibrated uncertainty, especially in low-data regimes.
arXiv Machine Learning
Aug 3
Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery
arXiv:2607. 29225v1 Announce Type: new Abstract: Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance.
arXiv Machine Learning
Jul 16
Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points
arXiv:2607. 14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing.
arXiv Machine Learning
Aug 12
Can Bayesian Optimization Efficiently Find a Strong Single Expert in Neural Thickets?
arXiv:2608. 10867v1 Announce Type: new Abstract: Gradient-free post-training has emerged as a compelling alternative to gradient-based optimization for large language models (LLMs), but existing approaches remain costly.
arXiv AI
Jun 2
c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for Expensive Hyperparameter Optimization
arXiv:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.