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A. Zharmagambetov*, A. Paulus*, C. Guo, B. Amos**, and Y. Tian**: "AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs".
(* = Equal 1st authors, ** = Equal advising)
[arXiv]
[code (coming soon)]
[NeurIPS] A. Zharmagambetov, B. Amos, A. Ferber, T. Huang, B. Dilkina, and Y. Tian: "Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information".
[arXiv]
[code]
A. Ferber, A. Zharmagambetov, T. Huang, B. Dilkina, and Y. Tian: "GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature".
[arXiv]
T. Huang, A. Ferber, A. Zharmagambetov, Y. Tian and B. Dilkina: "Contrastive Predict-and-Search for Mixed Integer Linear Programs".
[arXiv (coming soon)]
[Opt@NeurIPS workshop]
[CVPR] M. Gabidolla, M. Á. Carreira-Perpiñán, A. Zharmagambetov: "Towards better decision forests: Forest Alternating Optimization".
IEEE Conf. Computer Vision and Pattern Recognition, 2023.
[external link]
[paper preprint]
[supplementary material]
[animations]
[DMKD] S. S. Hada, M. Á. Carreira-Perpiñán, A. Zharmagambetov: "Sparse oblique decision trees: a tool to understand and manipulate neural net features".
Data Mining and Knowledge Discovery, 2023.
Many of the figures in the publisher's version are badly messed up, with wrong labels. Paper preprint has the correct figures.
[external link]
[paper preprint]
[NeurIPS] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization".
Advances in Neural Information Processing Systems (NeurIPS 2022).
[external link]
[paper preprint]
[short video]
[poster]
[AISTATS] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Learning Interpretable, Tree-Based Projection Mappings for Nonlinear Embeddings".
International Conference on Artificial Intelligence and Statistics (AISTATS 2022).
[external link]
[paper preprint]
[supplementary material]
[slides]
[poster]
[ICASSP] A. Zharmagambetov, Q. Tang , C.-C. Kao, Q. Zhang, M. Sun, V. Rozgic, J. Droppo, C. Wang: "Improved Representation Learning for Acoustic Event Classification Using Tree-structured Ontology".
IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2022).
[external link]
[paper preprint]
[slides]
[poster]
[EMNLP] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán: "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]
[ICASSP] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Learning a Tree of Neural Nets".
IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP).
[external link]
[paper preprint]
[poster]
[slides]
[IJCNN] A. Zharmagambetov and S. S. Hada and M. Gabidolla and M. Á. Carreira-Perpiñán: "Non-Greedy Algorithms for Decision Tree Optimization: An Experimental Comparison".
International Joint Conference on Neural Networks (IJCNN 2021).
[external link]
[paper preprint]
[arXiv version]
[IJCNN] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán: "Improved Boosted Regression Forests Through Non-Greedy Tree Optimization".
International Joint Conference on Neural Networks.
[external link]
[paper preprint]
[ICIP] A. Zharmagambetov and M. Gabidolla and M. Á. Carreira-Perpiñán: "Improved Multiclass AdaBoost for Image Classification:
the Role of Tree Optimization".
IEEE International Conference on Image Processing.
[external link]
[paper preprint]
[ICIP] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "A Simple, Effective Way to Improve Neural Net Classification:
Ensembling Unit Activations with a Sparse Oblique Decision Tree".
IEEE International Conference on Image Processing (ICIP 2021).
[external link]
[paper preprint]
[ICML] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Smaller, More Accurate Regression Forests Using Tree Alternating Optimization".
International Conference on Machine Learning (ICML 2020), Jul. 13, 2020.
[external link]
[paper preprint]
[supplementary material]
[slides]
[video]
[FODS] M. Á. Carreira-Perpiñán and A. Zharmagambetov: "Ensembles of Bagged TAO Trees Consistently Improve over Random Forests, AdaBoost and Gradient Boosting".
ACM-IMS Foundations of Data Science Conference
[external link]
[paper preprint]
[video]
[BayLearn] M. Gabidolla and A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Boosted Sparse Oblique Decision Trees".
Bay Area Machine Learning Symposium
[external link]
[paper preprint]
[BayLearn] M. Á. Carreira-Perpiñán and A. Zharmagambetov: "Fast Model Compression".
Bay Area Machine Learning Symposium
[external link]
[paper preprint]
[poster]
S. Narynov and A. Zharmagambetov (2016): "On One Approach of Solving Sentiment Analysis Task for Kazakh and Russian Languages Using Deep Learning".
Int. Conf. on Computational Collective Intelligence (ICCCI), Sep 2016.
[external link]
[paper preprint]
A. Zharmagambetov and A. A. Pak. (2015): "Sentiment Analysis of a Document using Deep Learning Approach and Decision Trees".
IEEE 12th International Conference on Electronics Computer and Computation, Almaty, Kazakhstan, 2015.
[external link]
A. A. Pak, S. Narynov, A. Zharmagambetov, Sh. Sagyndykova, Zh. Kenzhebayeva. (2015): "The Method of Synonyms Extraction from Unannotated Corpus".
IEEE Int. Conf. on Digital Information, Networking, and Wireless Communications (DINWC), Feb 2015.
[external link]
[paper preprint]
Learning Tree-Based Models with Manifold Regularization: Alternating Optimization Algorithms.
University of California, Merced, USA, 2022.
[external link]
[paper]
[slides]