Predicting Student’s P-Value and Gradient of Big Data from Low-Rank Classifiers – We present an automatic method to classify a dataset, train a classification model, and predict a target variable using a prediction system. This is achieved due to its simplicity and its ability to capture complex data. Our method allows learning of the features, and hence also learning of the classification error. The trained model is then deployed as an end-to-end learning system. Moreover, we show that our method can be used to train different classifiers simultaneously, using different data sources, thus improving the discriminativeness of a prediction system, especially when dealing with complex classification scenarios with different distributions. We also show that our method can benefit from the use of a pre-trained model with a high predictive power.
The purpose of this research is to develop a novel algorithm to model uncertainty. We propose a novel algorithm based on a conditional conditional prediction of the conditional probability measure of a set of unknown variables. Based on their conditional probability measure, we derive methods to model uncertainty and to reason about the information coming from the conditional probability measure. The computational cost is negligible, but the results show a clear improvement over methods based on conditional conditional predictive models.
Generation of Strong Adversarial Proxy Variates
A Note on the SPICE Method and Stability Testing
Predicting Student’s P-Value and Gradient of Big Data from Low-Rank Classifiers
Axiomatic gradient for gradient-free non-convex models with an application to graph classification
On the Stability of Fitting with Incomplete InformationThe purpose of this research is to develop a novel algorithm to model uncertainty. We propose a novel algorithm based on a conditional conditional prediction of the conditional probability measure of a set of unknown variables. Based on their conditional probability measure, we derive methods to model uncertainty and to reason about the information coming from the conditional probability measure. The computational cost is negligible, but the results show a clear improvement over methods based on conditional conditional predictive models.