69 citations · 222 across the 18 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Provable Adversarial Robustness for Fractional Lp Threat Models
Alexander Levine, Soheil Feizi
In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks…
Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation
Wenxiao Wang, Alexander Levine, Soheil Feizi
Data poisoning attacks aim at manipulating model behaviors through distorting training data. Previously, an aggregation-based certified defense, Deep Partition Aggregation (DPA), w…
Certifying Model Accuracy under Distribution Shifts
Aounon Kumar, Alexander Levine, Tom Goldstein +1
Certified robustness in machine learning has primarily focused on adversarial perturbations of the input with a fixed attack budget for each point in the data distribution. In this…