339 citations · 1k across the 41 of their papers we have counts for
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On Provable Benefits of Depth in Training Graph Convolutional Networks
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing. Despite the a…
Meta-learning with an Adaptive Task Scheduler
Huaxiu Yao, Yu Wang, Ying Wei +4
To benefit the learning of a new task, meta-learning has been proposed to transfer a well-generalized meta-model learned from various meta-training tasks. Existing meta-learning al…
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time
Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi
In this paper we prove that Local (S)GD (or FedAvg) can optimize deep neural networks with Rectified Linear Unit (ReLU) activation function in polynomial time. Despite the establis…
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…
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
Weilin Cong, Morteza Ramezani, Mehrdad Mahdavi
Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite their great success,…
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency
Yuyang Deng, Mehrdad Mahdavi
Local SGD is a promising approach to overcome the communication overhead in distributed learning by reducing the synchronization frequency among worker nodes. Despite the recent th…