352 citations · 802 across the 51 of their papers we have counts for
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Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection
Koby Bibas, Meir Feder, Tal Hassner
Detecting out-of-distribution (OOD) samples is vital for developing machine learning based models for critical safety systems. Common approaches for OOD detection assume access to…
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness
Uriya Pesso, Koby Bibas, Meir Feder
Adversarial attacks have been shown to be highly effective at degrading the performance of deep neural networks (DNNs). The most prominent defense is adversarial training, a method…
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression
Koby Bibas, Meir Feder
A fundamental principle of learning theory is that there is a trade-off between the complexity of a prediction rule and its ability to generalize. Modern machine learning models do…
Sequential prediction under log-loss and misspecification
Meir Feder, Yury Polyanskiy
We consider the question of sequential prediction under the log-loss in terms of cumulative regret. Namely, given a hypothesis class of distributions, learner sequentially predicts…