Koby Bibas, ``Large Scale Product Recognition for Improving Ad Performance’’, a talk in Retail Vision CVPR 2024 Workshop and Data Executive Event.
Koby Bibas, Shachar Shayovitz, and Meir Feder, ``Deep Individual Active Learning: Safeguarding Against Out-of-Distribution Challenges in Neural Networks’’. Entropy, 2024.
Koby Bibas, Oren Sar Shalom, and Dietmar Jannach, ``Semi-supervised Adversarial Learning for Complementary Item Recommendation’’. The ACM Web Conference, 2023.
Koby Bibas, Oren Sar Shalom, and Dietmar Jannach, ``Collaborative Image Understanding’’. The ACM International Conference on Information & Knowledge Management, 2022.
Koby Bibas, and Meir Feder, ``Beyond Ridge Regression for Distribution-Free Data’’. arXiv:2206.08757, 2022.
Koby Bibas, Meir Feder, and Tal Hassner, ``Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection’’. NeurIPS, 2021.
Koby Bibas, and Meir Feder, ``Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression’’. Workshop on Distribution-Free Uncertainty Quantification ICML, 2021.
Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, and Hayit Greenspan, ``Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction’’. The International Symposium on Biomedical Imaging (ISBI), 2021.
Koby Bibas, Yaniv Fogel, and Meir Feder, ``A New Look at an Old Problem: A Universal Learning Approach to Linear Regression’’. The IEEE International Symposium on Information Theory (ISIT), 2019.