Add to ORKGIn predictive modeling for high-stake decision-making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the confidence of prediction results with fewer theoretical assumptions. To obtain the confidence set by so-called full-CP, we need to refit the predictor for all possible values of prediction results, which is only possible for simple predictors. For complex predictors such as random forests (RFs) or neural networks (NNs), split-CP is often employed where the data is split into two parts: one part for fitting and another to compute the confidence set. Unfortunately, because of the reduced sample size, split-CP is inferior to full-CP both in fitting as well as confiden...
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-tar...
When data are stored in different locations and pooling of such data is not allowed, there is an inf...
Large and distributed data sets pose many challenges for machine learning, including requirements on...
Conformal prediction (CP) is a wrapper around traditional machine learning models, giving coverage g...
The talk reviews a modern machine learning technique called Conformal Predictors. The approach has b...
Conformal Prediction is a machine learning methodology that produces valid prediction regions under ...
Deep Learning predictions with measurable confidence are increasingly desirable for real-world probl...
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-tar...
Conformal prediction is a learning framework that produces models that associate with each of their ...
Safe deployment of deep neural networks in high-stake real-world applications require theoretically ...
One of the challenges with predictive modeling is how to quantify the reliability of the models' pre...
Making predictions with an associated confidence is highly desirable as it facilitates decision maki...
When one observes a sequence of variables $(x_1, y_1), \ldots, (x_n, y_n)$, Conformal Prediction (CP...
Regression conformal prediction produces prediction intervals that are valid, i.e.,the probability o...
Conformal prediction uses past experience to determine precise levels ofconfidence in new prediction...
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-tar...
When data are stored in different locations and pooling of such data is not allowed, there is an inf...
Large and distributed data sets pose many challenges for machine learning, including requirements on...
Conformal prediction (CP) is a wrapper around traditional machine learning models, giving coverage g...
The talk reviews a modern machine learning technique called Conformal Predictors. The approach has b...
Conformal Prediction is a machine learning methodology that produces valid prediction regions under ...
Deep Learning predictions with measurable confidence are increasingly desirable for real-world probl...
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-tar...
Conformal prediction is a learning framework that produces models that associate with each of their ...
Safe deployment of deep neural networks in high-stake real-world applications require theoretically ...
One of the challenges with predictive modeling is how to quantify the reliability of the models' pre...
Making predictions with an associated confidence is highly desirable as it facilitates decision maki...
When one observes a sequence of variables $(x_1, y_1), \ldots, (x_n, y_n)$, Conformal Prediction (CP...
Regression conformal prediction produces prediction intervals that are valid, i.e.,the probability o...
Conformal prediction uses past experience to determine precise levels ofconfidence in new prediction...
Ligand-based models can be used in drug discovery to obtain an early indication of potential off-tar...
When data are stored in different locations and pooling of such data is not allowed, there is an inf...
Large and distributed data sets pose many challenges for machine learning, including requirements on...