164 citations · 1.3k across the 73 of their papers we have counts for
13 papers · 1 filter
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Roman Levin, Manli Shu, Eitan Borgnia +3
Conventional saliency maps highlight input features to which neural network predictions are highly sensitive. We take a different approach to saliency, in which we identify and ana…
Adversarial Examples Make Strong Poisons
Liam Fowl, Micah Goldblum, Ping-yeh Chiang +3
The adversarial machine learning literature is largely partitioned into evasion attacks on testing data and poisoning attacks on training data. In this work, we show that adversari…
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova +3
Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usuall…
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild +2
Tabular data underpins numerous high-impact applications of machine learning from fraud detection to genomics and healthcare. Classical approaches to solving tabular problems, such…
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
Hossein Souri, Liam Fowl, Rama Chellappa +2
As the curation of data for machine learning becomes increasingly automated, dataset tampering is a mounting threat. Backdoor attackers tamper with training data to embed a vulnera…
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta +4
Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the…
The Intrinsic Dimension of Images and Its Impact on Learning
Phillip Pope, Chen Zhu, Ahmed Abdelkader +2
It is widely believed that natural image data exhibits low-dimensional structure despite the high dimensionality of conventional pixel representations. This idea underlies a common…
DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations
Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova +6
Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model. These attacks can be provably deflected using differentially private (DP…
Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release
Liam Fowl, Ping-yeh Chiang, Micah Goldblum +4
Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of releas…
Technical Challenges for Training Fair Neural Networks
Valeriia Cherepanova, Vedant Nanda, Micah Goldblum +2
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes setti…
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning
Jonas Geiping, Liam Fowl, Gowthami Somepalli +3
Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time. A variety of defenses against this threat model hav…
The Uncanny Similarity of Recurrence and Depth
Avi Schwarzschild, Arjun Gupta, Amin Ghiasi +2
It is widely believed that deep neural networks contain layer specialization, wherein neural networks extract hierarchical features representing edges and patterns in shallow layer…
LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition
Valeriia Cherepanova, Micah Goldblum, Harrison Foley +4
Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These…