28 citations · 52 across the 11 of their papers we have counts for
11 papers
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
Koby Bibas
Machine learning models have exhibited exceptional results in various domains. The most prevalent approach for learning is the empirical risk minimizer (ERM), which adapts the mode…
Semi-supervised Adversarial Learning for Complementary Item Recommendation
Koby Bibas, Oren Sar Shalom, Dietmar Jannach
Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like…
Collaborative Image Understanding
Koby Bibas, Oren Sar Shalom, Dietmar Jannach
Automatically understanding the contents of an image is a highly relevant problem in practice. In e-commerce and social media settings, for example, a common problem is to automati…
Beyond Ridge Regression for Distribution-Free Data
Koby Bibas, Meir Feder
In supervised batch learning, the predictive normalized maximum likelihood (pNML) has been proposed as the min-max regret solution for the distribution-free setting, where no distr…
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…