Papers and Reports
2025
- Aadit Jain, Fred J. Hickernell, Art B. Owen, and Aleksei G. Sorokin. “Empirical Bernstein and betting confidence intervals for randomized quasi-Monte Carlo.” arXiv preprint arXiv:2504.18677 (2025).
- Fred J. Hickernell, Nathan Kirk, and Aleksei G. Sorokin. “Quasi-Monte Carlo Methods: What, Why, and How?.” arXiv preprint arXiv:2502.03644 (2025).
- Ambrose Emmett-Iwaniw and Nathan Kirk. “Enhancing Neural Autoregressive Distribution Estimators for Image Reconstruction.” arXiv preprint arXiv:2506.05391 (2025).
- Nathan Kirk, T. Konstantin Rusch, Jakob Zech, and Daniela Rus. “Low Stein Discrepancy via Message-Passing Monte Carlo.” arXiv preprint arXiv:2503.21103 (2025).
- Nathan Kirk, Ivan Gvozdanović, and Sonja Petrović. “Multilevel Sampling in Algebraic Statistics.” arXiv preprint arXiv:2505.04062 (2025).
- Aleksei G. Sorokin. “A Unified Implementation of Quasi-Monte Carlo Generators, Randomization Routines, and Fast Kernel Methods.” arXiv preprint arXiv:2502.14256 (2025).
2024
- Aleksei G. Sorokin, Aleksandra Pachalieva, Daniel O’Malley, James M. Hyman, Fred J. Hickernell, and Nicolas W. Hengartner. “Computationally efficient and error aware surrogate construction for numerical solutions of subsurface flow through porous media.” Advances in Water Resources 193 (2024): 104836.
- Qian Shao, Jiangrui Kang, Qiyuan Chen, Zepeng Li, Hongxia Xu, Yiwen Cao, Jiajuan Liang, and Jian Wu. “Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection.” In The Thirty-eighth Annual Conference on Neural Information Processing Systems.
- Aleksei G. Sorokin, Xiaoyi Lu and Yi Wang. “A neural surrogate solver for radiation transfer.” In The Thirty-eighth Annual Conference on Neural Information Processing Systems Workshop D3S3.
2023
- Onyekachi Emenike, Fred J. Hickernell, and Peter Kritzer. “A unified treatment of tractability for approximation problems defined on Hilbert spaces.” Journal of Complexity 84 (2024): 101856.
- Aleksei G. Sorokin, Xinran Zhu, Eric Hans Lee, Bolong Cheng. “SigOpt Mulch: An intelligent system for AutoML of gradient boosted trees.” Knowledge-Based Systems 273 (2023): 110604.
- Aleksei G. Sorokin and Vishwas Rao. “Credible Intervals for Probability of Failure with Gaussian Processes.” arXiv preprint arXiv:2311.07733 (2023).
2022
- Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Jagadeeswaran Rathinavel, and Aleksei G. Sorokin. “Challenges in Developing Great Quasi-Monte Carlo Software.” International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing. Cham: Springer International Publishing, 2022.
- Aleksei G. Sorokin and Jagadeeswaran Rathinavel. “On Bounding and Approximating Functions of Multiple Expectations Using Quasi-Monte Carlo.” International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing. Cham: Springer International Publishing, 2022.
2021
- Aleksei G. Sorokin, Fred J. Hickernell, Sou-Cheng T. Choi, Michael J. McCourt, and Jagadeeswaran Rathinavel. “(Quasi-) Monte Carlo Importance Sampling with QMCPy.” Illinois Tech Undergraduate Research Journal 1. Illinois Institute of Technology, 2021.
- Sou-Cheng T. Choi, Yuhan Ding, Claude Jr Hall, Fred J. Hickernell, and Aleksei G. Sorokin. “Four Ways to Grow Scientific Software.” Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use. United States, 2021.
2020
- Sou-Cheng T. Choi, Fred J. Hickernell, Jagadeeswaran Rathinavel, Michael J. McCourt, and Aleksei G. Sorokin. “Quasi-Monte Carlo software.” International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing. Cham: Springer International Publishing, 2020.
2011-2019