P. Rademacher and M. Doroslovački, “Predictive Distribution Estimation for Bayesian Machine Learning using a Dirichlet Process Prior,” 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 1941-1945, doi: 10.1109/IEEECONF44664.2019.9048821.

P. Rademacher and M. Doroslovački, “Bayesian Learning for Classification using a Uniform Dirichlet Prior,” 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019, pp. 1-5, doi: 10.1109/GlobalSIP45357.2019.8969120.

P. Rademacher and K. Wagner, “Efficient Bayesian Sequential Classification Under the Markov Assumption for Various Loss Functions,” in IEEE Signal Processing Letters, vol. 27, pp. 401-405, 2020, doi: 10.1109/LSP.2020.2973854.

P. Rademacher and M. Doroslovački, “Bayesian Learning for Regression Using Dirichlet Prior Distributions of Varying Localization,” 2021 IEEE Statistical Signal Processing Workshop (SSP), 2021, pp. 236-240, doi: 10.1109/SSP49050.2021.9513745.

T. George, K. Wagner and P. Rademacher, “Deep Q-Network for Radar Task-Scheduling Problem,” 2022 IEEE Radar Conference (RadarConf22), 2022, pp. 1-5, doi: 10.1109/RadarConf2248738.2022.9764230.

P. Rademacher, K. Wagner and L. Smith, “Markov Decision Process Design for Imitation of Optimal Task Schedulers,” 2023 IEEE Statistical Signal Processing Workshop (SSP), Hanoi, Vietnam, 2023, pp. 56-60, doi: 10.1109/SSP53291.2023.10207940.