Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility ...
Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...
Training artificial intelligence models is costly. Researchers estimate that training costs for the largest frontier models ...
This project allows users to work with advanced portfolio optimization using natural language, without writing code. It provides 9 specialized MCP tools covering everything from classic mean-variance ...
Chances are, you’ve seen clicks to your website from organic search results decline since about May 2024—when AI Overviews launched. Large language model optimization (LLMO), a set of tactics for ...
Abstract: This study presents a two-phase framework that integrates machine learning with optimization modeling considering port authority behavior. First, a machine learning model predicts port ...
Abstract: In practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck ...
Nanotechnology and machine learning are transforming energy systems by enhancing engine efficiency and sustainability. The integration of advanced nanomaterials, such as gold nanoparticles (AuNPs), ...
ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...
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