Meisam Hejazinia

According to our database, Meisam Hejazinia authored at least 8 papers between 2019 and 2023.

Collaborative distances:

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

2023

Federated Ensemble Learning: Increasing the Capacity of Label Private Recommendation Systems.

Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu

IEEE Data Eng. Bull., 2023

Federated Linear Contextual Bandits with User-level Differential Privacy.

Ruiquan Huang, Huanyu Zhang, Luca Melis, Milan Shen, Meisam Hejazinia, Jing Yang

Proceedings of the International Conference on Machine Learning, 2023

2022

FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning.

Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis, Kiwan Maeng, Mani Malek, Luca Melis, Ilya Mironov, Milad Nasr, Kaikai Wang, Carole-Jean Wu

CoRR, 2022

2021

Wide Network Learning with Differential Privacy.

Huanyu Zhang, Ilya Mironov, Meisam Hejazinia

CoRR, 2021

2019

Accelerated learning from recommender systems using multi-armed bandit.

Meisam Hejazinia, Kyler Eastman, Shuqin Ye, Abbas Amirabadi, Ravi Divvela

CoRR, 2019

A/B Testing Measurement Framework for Recommendation Models Based on Expected Revenue.

Meisam Hejazinia, Majid Hosseini, Bryant Sih

CoRR, 2019

A Simple Deep Personalized Recommendation System.

Pavlos Mitsoulis-Ntompos, Meisam Hejazinia, Serena Zhang, Travis Brady

Proceedings of the Workshop on Recommenders in Tourism co-located with the 13th ACM Conference on Recommender Systems (RecSys 2019), 2019

Deep personalized re-targeting.

Meisam Hejazinia, Pavlos Mitsoulis-Ntompos, Serena Zhang

Proceedings of the ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining, 2019