20 citations · 27 across the 5 of their papers we have counts for
10 papers
FedRule: Federated Rule Recommendation System with Graph Neural Networks
Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng +3
Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera…
Learning Distributionally Robust Models at Scale via Composite Optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi +1
To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existin…
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
Sumanth Chennupati, Mohammad Mahdi Kamani, Zhongwei Cheng +1
Knowledge Distillation is becoming one of the primary trends among neural network compression algorithms to improve the generalization performance of a smaller student model with g…
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
Mohammad Mahdi Kamani, Rana Forsati, James Z. Wang +1
As algorithmic decision-making systems are becoming more pervasive, it is crucial to ensure such systems do not become mechanisms of unfair discrimination on the basis of gender, r…
Distributionally Robust Federated Averaging
Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
In this paper, we study communication efficient distributed algorithms for distributionally robust federated learning via periodic averaging with adaptive sampling. In contrast to…
Federated Learning with Compression: Unified Analysis and Sharp Guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari +1
In federated learning, communication cost is often a critical bottleneck to scale up distributed optimization algorithms to collaboratively learn a model from millions of devices w…