![[Image (JPEG): Magzhan Gabidolla, Merced, California, September 2023]](./files/profile.jpg) |
Magzhan GabidollaPhD CandidateElectrical 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] |
I am a 5th 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] .
[ICML] M. Gabidolla and A. Zharmagambetov and M. Á. Carreira-Perpiñán (2024): "Beyond the ROC Curve: Classification Trees Using Cost-Optimal Curves, with Application to Imbalanced Datasets".
International Conference on Machine Learning (ICML 2024)
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[paper preprint]
[poster]
[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)
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[paper preprint]
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[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)
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[paper preprint]
[supplementary material]
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[poster]
[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)
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[paper preprint]
[supplementary material]
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[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)
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[paper preprint]
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[video]
[UAI] R. Kairgeldin and M. Gabidolla and M. Á. Carreira-Perpiñán (2024): "Adaptive Softmax Trees for Many-Class Classification".
Conference on Uncertainty in Artificial Intelligence (UAI 2024)
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[paper preprint]
[poster]
[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 (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]
[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
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[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]
Machine Learning Engineer Intern at Snap Inc. Worked on compressing and accelerating the inference of diffusion models. Mentors: Yerlan Idelbayev and Dhritiman Sagar.
