A method for counterfactual explanation of machine learning survival models is proposed. One the difficulties solving problem that classes examples are implicitly defined through outcomes a model in form functions. condition establishes difference between functions original example and introduced. This based on using distance mean times to event. It shown can be reduced standard convex optimiza...

The evaluation of machine learning models is a crucial step before their application because it is essential to assess how well a model will behave for every single case. In many real applications, not only is it important to know the “total” or the “average” error of the model, it is also important to know how this error is distributed and how well confidence or probability estimations are mad...

In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature selection techniques in machine learning applications. The applied approaches obtain new feature directions on data, such that machine learning applications would show an increase in performance. We rev...

The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine Learning and Large Scale Optimization examines this interplay. Machine learning researchers have embraced the advances in mathematical programming allowing new types of models to be pursued. The special...

Machine learning (ML) relies on stochastic algorithms, all of which rely gradient approximations with \textquotedbl{}batch size\textquotedbl{} examples. Growing the batch size as optimization proceeds is a simple and usable method to reduce training time, provided that number workers grows size. In this work, we provide package trains PyTorch models Dask clusters, can grow if desired. Our simul...

Predicting rainfall is an important step in generating data for climate impact studies. Rainfall predictions are a key process providing assessments with inputs. A consistent pattern typically good normal plants; nevertheless, too much or little can be disastrous to crops, even deadly. Drought damage plants and lead erosion, while heavy encourage the growth of destructive fungi. Machine Learnin...

Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate training process that relies on min–max characterization of the optimal control and variables. Our main theoretical contribution is development posteriori estimates as tool evaluate convergence process. illustrate our results with numerical experiments for...