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Found 13 papers, 5 papers with code
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models.
From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery
Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "reward" over time, such as the maximum property value discovered so far.
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability.
Discovering Reliable Causal Rules
Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator.
Relative Flatness and Generalization
Flatness of the loss curve is conjectured to be connected to the generalization ability of machine learning models, in particular neural networks.
Adaptive Communication Bounds for Distributed Online Learning
The learning performance of such a protocol is intuitively optimal if approximately the same loss is incurred as in a hypothetical serial setting.
Communication-Efficient Distributed Online Learning with Kernels
It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion.
Discovering Reliable Correlations in Categorical Data
This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or the type of correlation, and, how to efficiently discover the top-most reliably correlated attribute sets from data.
Effective Parallelisation for Machine Learning
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications.
Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms
The reliable fraction of information is an attractive score for quantifying (functional) dependencies in high-dimensional data.
Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups
That is, these sub-populations are exceptional with regard to the global distribution.
Discovering Reliable Approximate Functional Dependencies
As we want to be agnostic on the form of the dependence, we adopt an information-theoretic approach, and construct a reliable, bias correcting score that can be efficiently computed.
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find.
