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Explores alternative message passing algorithms for cyclic graphs, challenging SPA's extrinsic principle and optimizing factor node updates to improve performance while maintaining simplicity.
Investigación sobre algoritmos polinomiales para el aprendizaje de estructuras en redes bayesianas con número de cobertura de vértices acotado, analizando su complejidad y posibles mejoras.
Explore causal representation learning and optimal intervention design with Caroline Uhler's insightful keynote, delving into cutting-edge AI research and its practical applications.
Innovative molecular representation learning method combining physics-driven parameter estimation and self-supervised learning for improved energy property estimation and structure discovery in material design.
Análisis del equilibrio entre contracción y desvinculación en aproximaciones gaussianas factorizadas para inferencia variacional, examinando déficits de incertidumbre y entropÃa en modelos de alta dimensión.
Técnica para reducir la varianza en estimadores de Monte Carlo con muestras limitadas, aprovechando similitudes entre tareas de integración relacionadas para mejorar el rendimiento.
Explore omitted variable bias in causal machine learning with Victor Chernozhukov's keynote, addressing challenges and solutions in this critical area of AI research.
Explores theoretical foundations of adversarial imitation learning with unknown transitions, proposing MB-TAIL algorithm for optimal expert sample and interaction complexity in reward-free exploration.
Presenta un enfoque novedoso para construir abstracciones de estado durante el aprendizaje por refuerzo, mejorando la eficiencia de muestreo y el rendimiento en múltiples dominios y problemas.
Explores optimal caching strategies for serverless computing, balancing cache retention costs and miss penalties. Derives solutions for Hawkes processes and evaluates performance using Azure Functions data.
Análisis teórico del aprendizaje a partir de datos tensoriales de bajo rango, comparando métodos que explotan esta estructura con enfoques estándar en clasificación y agrupamiento.
Explore novel methods for bounding continuous treatment effects with hidden confounders, offering tighter coverage and valuable insights for policy-makers using observational data.
Propone método de optimización bayesiana causal funcional para encontrar intervenciones que optimicen variables objetivo en grafos causales conocidos, extendiendo métodos CBO a intervenciones funcionales.
Explores testable implications and goodness-of-fit tests for missing data graphical models, focusing on sequential MAR/MNAR models for longitudinal studies and no self-censoring models for cross-sectional studies.
Presenta un nuevo procedimiento eficiente para establecer equivalencia de Markov entre grafos dirigidos cÃclicos y acÃclicos, basado en una reformulación del Teorema de Equivalencia CÃclica desde una perspectiva ancestral.
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