674 citations · 1.5k across the 15 of their papers we have counts for
16 papers
In-context Example Selection with Influences
Tai Nguyen, Eric Wong
In-context learning (ICL) is a powerful paradigm emerged from large language models (LLMs). Despite its promises, ICL performance is known to be highly sensitive to input examples.…
Black Box Adversarial Prompting for Foundation Models
Natalie Maus, Patrick Chao, Eric Wong +1
Prompting interfaces allow users to quickly adjust the output of generative models in both vision and language. However, small changes and design choices in the prompt can lead to…
A Data-Based Perspective on Transfer Learning
Saachi Jain, Hadi Salman, Alaa Khaddaj +3
It is commonly believed that in transfer learning including more pre-training data translates into better performance. However, recent evidence suggests that removing data from the…
When does Bias Transfer in Transfer Learning?
Hadi Salman, Saachi Jain, Andrew Ilyas +3
Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside. In this work, we demon…
Certified Patch Robustness via Smoothed Vision Transformers
Hadi Salman, Saachi Jain, Eric Wong +1
Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of…
DeepSplit: Scalable Verification of Deep Neural Networks via Operator Splitting
Shaoru Chen, Eric Wong, J. Zico Kolter +1
Analyzing the worst-case performance of deep neural networks against input perturbations amounts to solving a large-scale non-convex optimization problem, for which several past wo…