6 citations · 28 across the 8 of their papers we have counts for
8 papers
Text-To-Concept (and Back) via Cross-Model Alignment
Mazda Moayeri, Keivan Rezaei, Maziar Sanjabi +1
We observe that the mapping between an image's representation in one model to its representation in another can be learned surprisingly well with just a linear layer, even across d…
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Samyadeep Basu, Daniela Massiceti, Shell Xu Hu +1
Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent work…
Provable Robustness for Streaming Models with a Sliding Window
Aounon Kumar, Vinu Sankar Sadasivan, Soheil Feizi
The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to b…
CUDA: Convolution-based Unlearnable Datasets
Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi
Large-scale training of modern deep learning models heavily relies on publicly available data on the web. This potentially unauthorized usage of online data leads to concerns regar…
Run-Off Election: Improved Provable Defense against Data Poisoning Attacks
Keivan Rezaei, Kiarash Banihashem, Atoosa Chegini +1
In data poisoning attacks, an adversary tries to change a model's prediction by adding, modifying, or removing samples in the training data. Recently, ensemble-based approaches for…
Lethal Dose Conjecture on Data Poisoning
Wenxiao Wang, Alexander Levine, Soheil Feizi
Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding…