5 papers
Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
Cláudio Lúcio do Val Lopes, Lucca Machado da Silva
Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class an…
Partition-Guided Distance Saliency: Bridging Decision and Objective Spaces in Many-Objective Optimization
Cláudio Lúcio do Val Lopes, Flávio VinÃcius Cruzeiro Martins, Elizabeth Fialho Wanner
Explainability in Many-Objective Optimization (MaO) is currently hindered by the escalating complexity of the Pareto front, which renders the relationship between high-dimensional…
Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
Cláudio Lúcio Do Val Lopes, Lucca Machado da Silva, André de Oliveira Brandão
Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement. Standard single-objective models, including…
Engineering AI Agents for Clinical Workflows: A Case Study in Architecture,MLOps, and Governance
Cláudio Lúcio do Val Lopes, João Marcus Pitta, Fabiano Belém +2
The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and…
Assessing an evolutionary search engine for small language models, prompts, and evaluation metrics
Cláudio Lúcio do Val Lopes, Lucca Machado
The concurrent optimization of language models and instructional prompts presents a significant challenge for deploying efficient and effective AI systems, particularly when balanc…