ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization
arXiv:2603.21180v4 Announce Type: replace Abstract: Sequential experimental design under expensive, gradient-free objectives is a central challenge in computational statistics: evaluation budgets are tightly constrained and information must be extracted efficiently from each observation. We propose \textbf{ALMAB-DC}, a GP-based sequential design framework combining active learning, multi-armed bandits (MAB), and distributed asynchronous computing for expensive black-box experimentation. A Gaussian process surrogate with uncertainty-aware acquisition identifies informative query points; a UCB o
The increasing complexity and cost of AI model development and scientific experimentation necessitate more efficient and distributed optimization methods.
This development offers a significant efficiency gain in black-box optimization, crucial for advancing AI, drug discovery, and materials science where experiments are expensive and data is sparse.
The ALMAB-DC framework introduces a more robust and scalable approach to sequential experimental design, potentially accelerating research and development cycles in computationally intensive fields.
- · AI researchers
- · Biotech and pharma
- · Advanced materials science
- · Cloud computing providers
- · Organizations with inefficient experimental design
- · Traditional, non-parallelized optimization methods
More efficient resource utilization and accelerated discovery in fields reliant on expensive experimentation.
Reduced development costs for new AI models and scientific breakthroughs, leading to increased innovation.
Democratization of complex optimization tasks through accessible, distributed frameworks, lowering barriers to entry for advanced research.
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Read at arXiv cs.LG