163 citations · 1.4k across the 139 of their papers we have counts for
15 papers · 1 filter
Spuriosity Rankings: Sorting Data to Measure and Mitigate Biases
Mazda Moayeri, Wenxiao Wang, Sahil Singla +1
We present a simple but effective method to measure and mitigate model biases caused by reliance on spurious cues. Instead of requiring costly changes to one's data or model traini…
Invariant Learning via Diffusion Dreamed Distribution Shifts
Priyatham Kattakinda, Alexander Levine, Soheil Feizi
Though the background is an important signal for image classification, over reliance on it can lead to incorrect predictions when spurious correlations between foreground and backg…
Improved techniques for deterministic l2 robustness
Sahil Singla, Soheil Feizi
Training convolutional neural networks (CNNs) with a strict 1-Lipschitz constraint under the norm is useful for adversarial robustness, interpretable gradients and stable t…
Towards Improved Input Masking for Convolutional Neural Networks
Sriram Balasubramanian, Soheil Feizi
The ability to remove features from the input of machine learning models is very important to understand and interpret model predictions. However, this is non-trivial for vision mo…
Explicit Tradeoffs between Adversarial and Natural Distributional Robustness
Mazda Moayeri, Kiarash Banihashem, Soheil Feizi
Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of ro…
Goal-Conditioned Q-Learning as Knowledge Distillation
Alexander Levine, Soheil Feizi
Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during t…