111 citations · 1k across the 103 of their papers we have counts for
9 papers · 1 filter
Efficient and Local Parallel Random Walks
Michael Kapralov, Silvio Lattanzi, Navid Nouri +1
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning. Despite their relevance, the…
On the Certified Robustness for Ensemble Models and Beyond
Zhuolin Yang, Linyi Li, Xiaojun Xu +3
Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend agains…
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen +1
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs fr…
Reliable Graph Neural Network Explanations Through Adversarial Training
Donald Loveland, Shusen Liu, Bhavya Kailkhura +2
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have…
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti +2
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts. Unfortunately, efforts towards simu…
Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations
Hao Cheng, Kaidi Xu, Chenan Wang +3
To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximi…
Certifiably-Robust Federated Adversarial Learning via Randomized Smoothing
Cheng Chen, Bhavya Kailkhura, Ryan Goldhahn +1
Federated learning is an emerging data-private distributed learning framework, which, however, is vulnerable to adversarial attacks. Although several heuristic defenses are propose…
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network
James Diffenderfer, Bhavya Kailkhura
Recently, Frankle & Carbin (2019) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the…
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning
Xiaoyang Wang, Bo Li, Yibo Zhang +2
Several AutoML approaches have been proposed to automate the machine learning (ML) process, such as searching for the ML model architectures and hyper-parameters. However, these Au…