15 citations · 20 across the 10 of their papers we have counts for
12 papers
Generalized Graph Variational Autoencoders: Bounded Divergences Control Posterior Collapse
Kleyton da Costa, Bernardo Modenesi, Ivan F. M. Menezes +1
The variational graph autoencoder (VGAE) regularizes its posterior toward the prior with the Kullback-Leibler divergence, a choice inherited from the variational autoencoder rather…
Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLMs in pediatric encounters
Bernardo Modenesi, Jody Lin, Kimberly Kaphingst +4
Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in real clinical encounters, and…
When Should Graph Attention Be Sparse? Learning a Per-Edge Tsallis Index
Kleyton da Costa, Bernardo Modenesi
Graph attention normalizes neighborhood scores with softmax, the maximum-entropy choice under Shannon statistics. But homophilic and heterophilic graphs want different attention sh…
Perspectives on Tsallis Statistics for Artificial Intelligence
Kleyton da Costa, Bernardo Modenesi
Tsallis statistics generalizes Boltzmann-Gibbs statistical mechanics through a single real parameter that controls the weight assigned to rare and frequent events. Originally p…
GraphNetz: Statistical Benchmarking of Graph Neural Networks with Paired Tests and Rank Aggregation
Kleyton da Costa, Bernardo Modenesi
Graph Neural Networks (GNNs) benchmarks often report single point estimates, even when performance differences are small relative to variation across random seeds, train/test split…
Divergence-Guided Particle Swarm Optimization
Kleyton da Costa, Bernardo Modenesi, Ivan F. M. Menezes +1
Particle Swarm Optimization (PSO) is susceptible to premature convergence when the swarm collapses around the global best, particularly on multimodal landscapes in higher dimension…
Generalized Machine Learning for Fast Calibration of Agent-Based Epidemic Models
Sima Najafzadehkhoei, George Vega Yon, Derek S. Meyer +1
Agent-based models (ABMs) are widely used to study infectious disease dynamics, but their calibration is often computationally intensive, limiting their applicability in time-sensi…
Advancing Distribution Decomposition Methods Beyond Common Supports: Applications to Racial Wealth Disparities
Bernardo Modenesi
I generalize state-of-the-art approaches that decompose differences in the distribution of a variable of interest between two groups into a portion explained by covariates and a re…
Detailed Gender Wage Gap Decompositions: Controlling for Worker Unobserved Heterogeneity Using Network Theory
Jamie Fogel, Bernardo Modenesi
Recent advances in the literature of decomposition methods in economics have allowed for the identification and estimation of detailed wage gap decompositions. In this context, bui…
An Interdisciplinary Outlook on Large Language Models for Scientific Research
James Boyko, Joseph Cohen, Nathan Fox +9
In this paper, we describe the capabilities and constraints of Large Language Models (LLMs) within disparate academic disciplines, aiming to delineate their strengths and limitatio…
What is a Labor Market? Classifying Workers and Jobs Using Network Theory
Jamie Fogel, Bernardo Modenesi
This paper develops a new data-driven approach to characterizing latent worker skill and job task heterogeneity by applying an empirical tool from network theory to large-scale Bra…
Evaluating Explainability in Machine Learning Predictions through Explainer-Agnostic Metrics
Cristian Munoz, Kleyton da Costa, Bernardo Modenesi +1
The rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in governance and regulation, particularly regarding the understanding o…