184 citations · 284 across the 13 of their papers we have counts for
23 papers
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
Marco Bornstein, Tahseen Rabbani, Evan Wang +2
The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train…
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Yongyuan Liang, Yanchao Sun, Ruijie Zheng +1
Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations. Therefore, it i…
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
Mucong Ding, Kezhi Kong, Jingling Li +4
Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To sca…
Practical and Fast Momentum-Based Power Methods
Tahseen Rabbani, Apollo Jain, Arjun Rajkumar +1
The power method is a classical algorithm with broad applications in machine learning tasks, including streaming PCA, spectral clustering, and low-rank matrix approximation. The di…
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Huimin Zeng, Jiahao Su, Furong Huang
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against -norm adversarial perturba…
Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang
Enforcing orthogonality in neural networks is an antidote for gradient vanishing/exploding problems, sensitivity by adversarial perturbation, and bounding generalization errors. Ho…