Print Email Facebook Twitter How effective are minimax methods in mitigating sample selection bias? Title How effective are minimax methods in mitigating sample selection bias? Author Khan, Zeeshan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Tepeli, Y.I. (mentor) P. Gonçalves, Joana (mentor) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Sample selection bias is a well-known problem in machine learning, where the source and target data distributions differ, leading to biased predictions and difficulties in generalization. This bias presents significant challenges for modern machine learning algorithms. To tackle this problem, researchers have focused on developing domain adaptation techniques that aim to create robust methods against sample selection bias. One approach is the use of minimax estimation techniques, which belong to the category of inference-based techniques. Despite the extensive research in developing these domain adaptation methods, there remains a critical need to evaluate their performance. This thesis explores the performance differences of various minimax estimation techniques in the presence of sample selection bias, providing insights into their effectiveness in mitigating the challenges posed by biased data. By understanding and evaluating the performance of these techniques, this research contributes to the advancement of domain adaptation methods and their application in real-world machine learning scenarios. Subject sample selection biasdomain adaptationmini-max To reference this document use: http://resolver.tudelft.nl/uuid:4137a28f-2e00-4dc2-865c-a1eb7bd878f4 Part of collection Student theses Document type bachelor thesis Rights © 2023 Zeeshan Khan Files PDF CSE3000_How_effective_are ... n_bias.pdf 674.28 KB Close viewer /islandora/object/uuid:4137a28f-2e00-4dc2-865c-a1eb7bd878f4/datastream/OBJ/view