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01/31/2023
Bayesian estimation of information-theoretic metrics for sparsely sampled distributions
Estimating the Shannon entropy of a discrete distribution from which we ...
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11/03/2022
Differences in collaboration structures and impact among prominent researchers in Europe and North America
Scientists collaborate through intricate networks, which impact the qual...
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04/06/2022
Fundamental limits to learning closed-form mathematical models from data
Given a finite and noisy dataset generated with a closed-form mathematic...
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03/26/2021
Node metadata can produce predictability transitions in network inference problems
Network inference is the process of learning the properties of complex n...
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03/09/2021
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies
Many studies have shown that there are regularities in the way human bei...
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04/25/2020
A Bayesian machine scientist to aid in the solution of challenging scientific problems
Closed-form, interpretable mathematical models have been instrumental fo...
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02/10/2020
Network-based models for social recommender systems
With the overwhelming online products available in recent years, there i...
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03/05/2018
Tensorial and bipartite block models for link prediction in layered networks and temporal networks
Many real-world complex systems are well represented as multilayer netwo...
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05/22/2017
On the consistency between model selection and link prediction in networks
A principled approach to understand network structures is to formulate g...
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10/17/2012
Justice blocks and predictability of US Supreme Court votes
Successful attempts to predict judges' votes shed light into how legal d...
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10/03/2012