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Artificial Intelligence
Psychology
Web Development
Comprendere la filosofia
Perdón y reconciliación: cómo sanar heridas
Arab-Islamic History: From Tribes to Empires
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Explora el poder de las redes neuronales para resolver problemas de optimización NP-hard, enfocándose en problemas de satisfacción de restricciones y su relación con la programación semidefinida.
Explores gradient descent's role in feature learning across various models, revealing spectral bias phenomena and implications for modern machine learning paradigms.
Explore classical and free zero bias transformations in probability theory, focusing on their applications to infinite divisibility and related concepts.
Explore the connection between Hamilton-Jacobi-Bellman equations and multi-armed bandit problems, leading to an efficient algorithm for sequential decision-making under uncertainty.
Explore random interface growth phenomena using probability, PDEs, and integrable systems to understand atypical behavior of the KPZ equation in various contexts like tumors and infections.
Explore sample amplification: generating more data from limited samples, even when learning the distribution is impossible. Discover bounds for various distribution families.
Explore latent graphical model estimation for multimodal functional data, focusing on brain connectivity analysis using simultaneous imaging techniques.
Exploración de la distribución de Boltzmann: caracterización única, independencia en sistemas desacoplados y simetrÃas en semigrupos de convolución y polinomios.
Explore advancements in mean and location estimation, including new estimators and characterizations that yield optimal constants, with applications in high-dimensional regimes and improvements over MLE.
Explore geometric constructions for sparse integer signal recovery using integer matrices, with applications in compressed sensing, wireless communications, and medical imaging.
Explore differentially private stochastic optimization with non-uniform Lipschitz losses, addressing outliers and heavy-tailed data for improved excess risk bounds in machine learning applications.
Explore online k-means clustering for arbitrary data streams, focusing on a novel algorithm with polynomial space and time complexity, offering provable guarantees without input data assumptions.
Explora la estabilidad espectral bajo perturbaciones aleatorias, analizando cómo pequeñas variaciones en matrices afectan sus eigenvalores y eigenvectores, con aplicaciones en álgebra lineal numérica.
Explora el clustering de mezclas gaussianas con covarianza desconocida, analizando un problema Max-Cut y desarrollando un algoritmo iterativo eficiente, con énfasis en la brecha estadÃstico-computacional.
Generalized Kyle-Back insider trading model with dynamic information, exploring Markovization, stochastic two-point boundary value problems, and forward-backward SDEs for determining pricing rule functions and equilibrium.
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