Zaharah Bukhsh

According to our database, Zaharah Bukhsh authored at least 9 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Self-supervise ensemble for extreme imbalance data streams with concept drift.

Yiming Teng, Zaharah Bukhsh, Yingqian Zhang, Hang Yin

Neurocomputing, 2026

2025

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers, Charalampos Andriotis, Yingqian Zhang, Zaharah Bukhsh

Neural Comput. Appl., October, 2025

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data.

Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang

CoRR, September, 2025

Offline reinforcement learning for learning to dispatch for job shop scheduling.

Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang

Mach. Learn., August, 2025

Graph neural networks for job shop scheduling problems: A survey.

Igor G. Smit, Jianan Zhou, Robbert Reijnen, Yaoxin Wu, Jian Chen, Cong Zhang, Zaharah Bukhsh, Yingqian Zhang, Wim Nuijten

Comput. Oper. Res., 2025

Deep Reinforcement Learning Based Genetic Framework for Flexible Job-Shop Scheduling Under Practical Constraints.

Kjell van Straaten, Robbert Reijnen, Zaharah Bukhsh, Yaoxin Wu, Yingqian Zhang

Proceedings of the Learning and Intelligent Optimization - 19th International Conference, 2025

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization.

Robbert Reijnen, Yaoxin Wu, Zaharah Bukhsh, Yingqian Zhang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Search Trajectory Network-Enhanced Multi-Objective Dynamic Algorithm Configuration.

Robbert Reijnen, Zaharah Bukhsh, Hoong Chuin Lau, Yaoxin Wu, Yingqian Zhang

Proceedings of the ECAI 2025 - 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy, 2025

2024

Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?

Roel Bouman, Zaharah Bukhsh, Tom Heskes

J. Mach. Learn. Res., 2024