Balancing Specialization, Generalization, and Compression for Detection and Tracking. - Dotan Kaufman; Koby Bibas; Eran Borenstein; Michael Chertok; Tal Hassner (BMVC, 2019)

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression. - Koby Bibas; Yaniv Fogel; Meir Feder (ISIT, 2019)

Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks. - Koby Bibas; Yaniv Fogel; Meir Feder (CoRR, 2019)

Learning Rotation Invariant Features For Cryogenic Electron Microscopy Image Reconstruction. - Koby Bibas; Gili Weiss-Dicker; Dana Cohen 002; Noa Cahan; Hayit Greenspan (ISBI, 2021)

Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection. - Koby Bibas; Meir Feder; Tal Hassner (NeurIPS, 2021)

The Predictive Normalized Maximum Likelihood for Over-parameterized Linear Regression with Norm Constraint: Regret and Double Descent. - Koby Bibas; Meir Feder (CoRR, 2021)

Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness. - Uriya Pesso; Koby Bibas; Meir Feder (CoRR, 2021)

Collaborative Image Understanding. - Koby Bibas; Oren Sar Shalom; Dietmar Jannach (CIKM, 2022)

Beyond Ridge Regression for Distribution-Free Data. - Koby Bibas; Meir Feder (CoRR, 2022)

Semi-supervised Adversarial Learning for Complementary Item Recommendation. - Koby Bibas; Oren Sar Shalom; Dietmar Jannach (WWW, 2023)

Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data. - Koby Bibas (CoRR, 2024)

Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks. - Shachar Shayovitz; Koby Bibas; Meir Feder (Entropy, 2024)