958 citations · 1.2k across the 17 of their papers we have counts for
7 papers · 1 filter
Test-Time Adaptation via Conjugate Pseudo-labels
Sachin Goyal, Mingjie Sun, Aditi Raghunathan +1
Test-time adaptation (TTA) refers to adapting neural networks to distribution shifts, with access to only the unlabeled test samples from the new domain at test-time. Prior TTA met…
(Certified!!) Adversarial Robustness for Free!
Nicholas Carlini, Florian Tramer, Krishnamurthy Dj Dvijotham +3
In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To…
Poisoned classifiers are not only backdoored, they are fundamentally broken
Mingjie Sun, Siddhant Agarwal, J. Zico Kolter
Under a commonly-studied backdoor poisoning attack against classification models, an attacker adds a small trigger to a subset of the training data, such that the presence of this…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
Characterizing Attacks on Deep Reinforcement Learning
Xinlei Pan, Chaowei Xiao, Warren He +8
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to adversarial attacks, which attack DRL models by adding small perturbations to the observations.…
Data Poisoning Attack against Unsupervised Node Embedding Methods
Mingjie Sun, Jian Tang, Huichen Li +4
Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec) have attracted growing interests given their simplicity and effectiveness. However, although these methods…
Rethinking the Value of Network Pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou +2
Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training…