6 citations · 14 across the 10 of their papers we have counts for
11 papers
Improving Group Lasso for high-dimensional categorical data
Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel +1
Sparse modelling or model selection with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or…
Double logistic regression approach to biased positive-unlabeled data
Konrad Furmańczyk, Jan Mielniczuk, Wojciech Rejchel +1
Positive and unlabelled learning is an important problem which arises naturally in many applications. The significant limitation of almost all existing methods lies in assuming tha…
Group Lasso merger for sparse prediction with high-dimensional categorical data
Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel
Sparse prediction with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group L…
Structure learning for CTBN's via penalized maximum likelihood methods
Maryia Shpak, Błażej Miasojedow, Wojciech Rejchel
The continuous-time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions…
Improving Lasso for model selection and prediction
Piotr Pokarowski, Wojciech Rejchel, Agnieszka Soltys +2
It is known that the Thresholded Lasso (TL), SCAD or MCP correct intrinsic estimation bias of the Lasso. In this paper we propose an alternative method of improving the Lasso for p…
Rank-based Lasso -- efficient methods for high-dimensional robust model selection
Wojciech Rejchel, Malgorzata Bogdan
We consider the problem of identifying significant predictors in large data bases, where the response variable depends on the linear combination of explanatory variables through an…
Asymptotics of maximum likelihood estimators based on Markov chain Monte Carlo methods
Błażej Miasojedow, Wojciech Niemiro, Wojciech Rejchel
In many complex statistical models maximum likelihood estimators cannot be calculated. In the paper we solve this problem using Markov chain Monte Carlo approximation of the true l…
Sparse estimation in Ising Model via penalized Monte Carlo methods
Błażej Miasojedow, Wojciech Rejchel
We consider a problem of model selection in high-dimensional binary Markov random fields. The usefulness of the Ising model in studying systems of complex interactions has been con…
Oracle inequalities for ranking and U-processes with Lasso penalty
Wojciech Rejchel
We investigate properties of estimators obtained by minimization of U-processes with the Lasso penalty in high-dimensional settings. Our attention is focused on the ranking problem…
Asymptotics of Monte Carlo maximum likelihood estimators
Blazej Miasojedow, Wojciech Niemiro, Jan Palczewski +1
We describe Monte Carlo approximation to the maximum likelihood estimator in models with intractable norming constants and explanatory variables. We consider both sources of random…
Adaptive Monte Carlo Maximum Likelihood
Blazej Miasojedow, Wojciech Niemiro, Jan Palczewski +1
We consider Monte Carlo approximations to the maximum likelihood estimator in models with intractable norming constants. This paper deals with adaptive Monte Carlo algorithms, whic…