Arman ZharmagambetovPostdoctoral ResearcherMeta AI (FAIR) Menlo Park, California, USA Email: armanz [at] meta [dot] com azharmagambetov [at] ucmerced [dot] edu |
I am a postdoctoral researcher in Fundamental AI Research (FAIR) at Meta , where I am fortunate to work with Yuandong Tian, Brandon Amos and Chuan Guo. My research is broadly on machine learning and optimization, recently involving AI-guided optimization, reinforcement learning and combinatorial optimization.
I completed my Ph.D. at University of California, Merced (UCM) advised by Miguel Á. Carreira-Perpiñán where I primarily studied learning algorithms for decision trees and tree-based models (see TAO). Check out my [CV].
A. Zharmagambetov*, A. Paulus*, C. Guo, B. Amos**, and Y. Tian** (2024): "AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs".
(* = Equal 1st authors, ** = Equal advising)
[arXiv]
[code]
[NeurIPS] A. Zharmagambetov, B. Amos, A. Ferber, T. Huang, B. Dilkina, and Y. Tian (2023): "Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information".
Advances in Neural Information Processing Systems, 2023.
[arXiv]
[code]
[ICML] A. Ferber, A. Zharmagambetov, T. Huang, B. Dilkina, and Y. Tian (2023): "GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature".
International Conference on Machine Learning, 2024 (to appear).
[arXiv]
[NeurIPS] A. Zharmagambetov and M. Á. Carreira-Perpiñán (2022): "Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization".
Advances in Neural Information Processing Systems, 2022.
[external link]
[paper preprint]
[short video]
[poster]