Magzhan Gabidolla

PhD Candidate Electrical Engineering and Computer Science (EECS) School of Engineering University of California, Merced 5200 N. Lake Road Merced, CA 95343 Email: mgabidolla [at] ucmerced.edu WWW: http://mgabidolla.github.io Social: [LinkedIn]

Research interests

My research focus is on Machine Learning and Optimization. My recent projects are on the topic of learning decision trees, forests and tree-based models; and their application in various domains: supervised learning, clustering, neural network compression, and model interpretability.

About

I am a 4th year PhD candidate at University of California, Merced (UCM) supervised by Miguel Á. Carreira-Perpiñán. I hold a BSc degree in Computer Science from NU in Astana, Kazakhstan. You can find my CV [here] .

Publications

[Google Scholar page]

[KDD] M. Gabidolla and M. Á. Carreira-Perpiñán (2022): "Optimal Interpretable Clustering Using Oblique Decision Trees".
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)
[external link] [paper preprint] [slides] [poster]
[CVPR] M. Gabidolla and M. Á. Carreira-Perpiñán (2022): "Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees".
Conference on Computer Vision and Pattern Recognition (CVPR 2022)
[external link] [paper preprint] [supplementary material] [slides] [poster]
[BayLearn] M. Gabidolla and A. Zharmagambetov and M. Á. Carreira-Perpiñán (2023): "Cost-sensitive learning of classification trees, with application to imbalanced datasets.".
Bay Area Machine Learning Symposium (BayLearn 2023), Oct. 19, 2023
[external link] [paper preprint]
[CVPR] M. Á. Carreira-Perpiñán and M. Gabidolla and A. Zharmagambetov (2023): "Towards Better Decision Forests: Forest Alternating Optimization".
Conference on Computer Vision and Pattern Recognition (CVPR 2023)
[external link] [paper preprint] [supplementary material] [slides] [poster] [2D-demo]
[EMNLP] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán (2021): "Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large".
Conference on Empirical Methods in Natural Language Processing (EMNLP 2021, long paper track)
[external link] [paper preprint] [slides] [poster] [video]
[IJCNN] M. Gabidolla and A. Zharmagambetov and M. Á. Carreira-Perpiñán (2022): "Improved multiclass AdaBoost using sparse oblique decision trees".
International Joint Conference on Neural Networks (IJCNN 2022), Jul. 18, 2022.
[external link] [paper preprint] [slides]
[BayLearn] M. Gabidolla and A. Zharmagambetov and M. Á. Carreira-Perpiñán (2020): "Boosted Sparse Oblique Decision Trees".
Bay Area Machine Learning Symposium (BayLearn 2020), Oct. 15, 2020
[external link] [paper preprint]
[ICIP] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán (2021): "Improved Multiclass AdaBoost for Image Classification: the Role of Tree Optimization".
IEEE International Conference on Image Processing (ICIP 2021), Sep. 19, 2021.
[external link] [paper preprint]
[IJCNN] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán (2021): "Improved Boosted Regression Forests Through Non-Greedy Tree Optimization".
International Joint Conference on Neural Networks (IJCNN 2021), Jul. 18, 2021.
[external link] [paper preprint]
[IJCNN] A. Zharmagambetov and S. S. Hada and M. Gabidolla and M. Á. Carreira-Perpiñán (2021): "Non-Greedy Algorithms for Decision Tree Optimization: An Experimental Comparison".
International Joint Conference on Neural Networks (IJCNN 2021), Jul. 18, 2021.
[external link] [paper preprint] [arxiv version]

Professional Activities

Reviewer, Journal of Machine Learning Research (JMLR 2022, 2023).
Reviewer, Int. Conference on Artificial Intelligence and Statistics (AISTATS 2022, 2023).
Student Volunteer, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022).

Awards

KDD 2022 Student Travel Award ($800).
CVPR 2022 Travel Grant Award ($300).