On Ranking and Generalization Bounds. - Wojciech Rejchel (J. Mach. Learn. Res., 2012)

Fast rates for ranking with large families. - Wojciech Rejchel (Neurocomputing, 2015)

Comments and Correction on "U-Processes and Preference Learning" (Neural Computation Vol. 26, pp. 2896-2924, 2014). - Wojciech Rejchel; Hong Li 009; Chuanbao Ren; Luoqing Li (Neural Comput., 2015)

Adaptive Monte Carlo Maximum Likelihood. - Blazej Miasojedow; Wojciech Niemiro; Jan Palczewski; Wojciech Rejchel (Challenges in Computational Statistics and Data Mining, 2016)

Oracle inequalities for ranking and U-processes with Lasso penalty. - Wojciech Rejchel (Neurocomputing, 2017)

Sparse Estimation in Ising Model via Penalized Monte Carlo Methods. - Blazej Miasojedow; Wojciech Rejchel (J. Mach. Learn. Res., 2018)

Structure learning for CTBN's via penalized maximum likelihood methods. - Maryia Shpak; Blazej Miasojedow; Wojciech Rejchel (CoRR, 2020)

Prediction and Variable Selection in High-Dimensional Misspecified Binary Classification. - Konrad Furmanczyk; Wojciech Rejchel (Entropy, 2020)

Joint estimation of posterior probability and propensity score function for positive and unlabelled data. - Konrad Furmanczyk; Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (CoRR, 2022)

Double Logistic Regression Approach to Biased Positive-Unlabeled Data. - Konrad Furmanczyk; Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (ECAI, 2023)

Improving Group Lasso for High-Dimensional Categorical Data. - Szymon Nowakowski; Piotr Pokarowski; Wojciech Rejchel; Agnieszka Soltys (ICCS (2), 2023)

Joint empirical risk minimization for instance-dependent positive-unlabeled data. - Wojciech Rejchel; Pawel Teisseyre; Jan Mielniczuk (Knowl. Based Syst., 2024)

Class prior estimation for positive-unlabeled learning when label shift occurs. - Jan Mielniczuk; Wojciech Rejchel; Pawel Teisseyre (CoRR, 2025)