Saito, S., & Herbster, M. (2022). Generalizing p-Laplacian: spectral hypergraph theory and a partitioning algorithm. Machine Learning. doi:10.1007/s10994-022-06264-y

Yang, L., Chen, Y. Z. J., Hajiesmaili, M. H., Herbster, M., & Towsley, D. (2022). Hierarchical Learning Algorithms for Multi-scale Expert Problems. Presented at: SIGMETRICS/PERFORMANCE 2022 - 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, null. doi:10.1145/3489048.3530967

Yang, L., Chen, Y. Z. J., Hajiesmaili, M. H., Herbster, M., & Towsley, D. (2022). Hierarchical Learning Algorithms for Multi-scale Expert Problems. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6 (2). doi:10.1145/3530900

Herbster, M., Pasteris, S., Vitale, F., & Pontil, M. (2021). A Gang of Adversarial Bandits. Advances in Neural Information Processing Systems, 3, 2265-2279.

Robinson, J., & Herbster, M. (2021). Improved Regret Bounds for Tracking Experts with Memory.

Herbster, M., Pasteris, S., & Tse, L. (2021). Online Matrix Completion with Side Information. Advances in Neural Information Processing Systems 33.

Herbster, M., Pasteris, S., & Tse, L. (2021). Online Multitask Learning with Long-Term Memory. Advances in Neural Information Processing Systems 33.

Pasteris, S., Vitale, F., Chan, K., Shiqiang, W., & Herbster, M. (2019). MaxHedge: Maximising a Maximum Online. Proceedings of Machine Learning Research. PMLR.

Herbster, M., & Robinson, J. (2019). Online Prediction of Switching Graph Labelings with Cluster Specialists. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 32. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).

Pasteris, S., Shiqiang, W., Herbster, M., & He, T. (2019). Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE.