6 citations · 14 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…