164 citations · 1.3k across the 73 of their papers we have counts for
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Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses
Micah Goldblum, Dimitris Tsipras, Chulin Xie +6
As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve…
Analyzing the Machine Learning Conference Review Process
David Tran, Alex Valtchanov, Keshav Ganapathy +4
Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the mac…
Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff
Eitan Borgnia, Valeriia Cherepanova, Liam Fowl +5
Data poisoning and backdoor attacks manipulate victim models by maliciously modifying training data. In light of this growing threat, a recent survey of industry professionals reve…
An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process
David Tran, Alex Valtchanov, Keshav Ganapathy +4
Mainstream machine learning conferences have seen a dramatic increase in the number of participants, along with a growing range of perspectives, in recent years. Members of the mac…
Random Network Distillation as a Diversity Metric for Both Image and Text Generation
Liam Fowl, Micah Goldblum, Arjun Gupta +2
Generative models are increasingly able to produce remarkably high quality images and text. The community has developed numerous evaluation metrics for comparing generative models.…
Data Augmentation for Meta-Learning
Renkun Ni, Micah Goldblum, Amr Sharaf +2
Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation s…
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
Avi Schwarzschild, Micah Goldblum, Arjun Gupta +2
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference. A recent survey of industry practitioners found that data poisoning…
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
Micah Goldblum, Avi Schwarzschild, Ankit B. Patel +1
Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain. Nonetheless, little is known about the robustness…
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
Micah Goldblum, Steven Reich, Liam Fowl +3
Meta-learning algorithms produce feature extractors which achieve state-of-the-art performance on few-shot classification. While the literature is rich with meta-learning methods,…