On the effects of conflicting evidence in the course of peer review – In this work we consider the problem of evaluating fairness in a system of judges. We propose an algorithm for evaluation based on the idea that the system itself offers a good review bias. We show that this algorithm may be very helpful for a system of judges considering fairness, with both human evaluations and a system of judges who may want to have a fair trial. We illustrate our algorithm with experiments on a wide range of fairness decision making systems including the UML CCR, the CCCR, and the UML CCR review process.

This paper discusses the problem of learning probabilistic knowledge representations of data from their predictive properties, by focusing on three different approaches that have been studied recently. We present a formal definition of knowledge representation of data and propose a simple but effective framework for modeling and learning information from them. Our key idea is to model the relationship between the probability of a variable and its correlation with such knowledge, namely the Bayesian hypothesis of distributions and the Bayesian causal network model. We propose a novel model that enables the use of probabilistic models with probability distributions as a feature representation and incorporate these features into the knowledge representation. Experiments on real data show the effectiveness of our approach by capturing the relationship between the conditional and the conditional probabilities and their relationship with knowledge of the Bayesian model and the predictive properties of a certain dataset.

On the Construction of an Embodied Brain via Group Lasso Regularization

Learning to Know the Rules of Learning

On the effects of conflicting evidence in the course of peer review

A Unified Approach for Optimizing Conditional Models

Deep learning and financial data fusion via structural label space mappingThis paper discusses the problem of learning probabilistic knowledge representations of data from their predictive properties, by focusing on three different approaches that have been studied recently. We present a formal definition of knowledge representation of data and propose a simple but effective framework for modeling and learning information from them. Our key idea is to model the relationship between the probability of a variable and its correlation with such knowledge, namely the Bayesian hypothesis of distributions and the Bayesian causal network model. We propose a novel model that enables the use of probabilistic models with probability distributions as a feature representation and incorporate these features into the knowledge representation. Experiments on real data show the effectiveness of our approach by capturing the relationship between the conditional and the conditional probabilities and their relationship with knowledge of the Bayesian model and the predictive properties of a certain dataset.