A New Look at an Old Problem: A Universal Learning Approach to Linear Regression K Bibas, Y Fogel, M Feder The 2019 IEEE International Symposium on Information Theory (ISIT), 2019 | 42 | 2019 |
Single layer predictive normalized maximum likelihood for out-of-distribution detection K Bibas, M Feder, T Hassner Advances in Neural Information Processing Systems 34, 1179-1191, 2021 | 23 | 2021 |
Deep pnml: Predictive normalized maximum likelihood for deep neural networks K Bibas, Y Fogel, M Feder arXiv preprint arXiv:1904.12286, 2019 | 20 | 2019 |
Balancing Specialization, Generalization, and Compression for Detection and Tracking D Kaufman, K Bibas, E Borenstein, M Chertok, T Hassner British Machine Vision Conference (BMVC), 2019 | 7 | 2019 |
Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression K Bibas, M Feder Workshop on Distribution-Free Uncertainty Quantification ICML 2021, 2021 | 6* | 2021 |
Learning Rotation Invariant Features for Cryogenic Electron Microscopy Image Reconstruction K Bibas, G Weiss-Dicker, D Cohen, N Cahan, H Greenspan 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021 | 5 | 2021 |
Semi-supervised Adversarial Learning for Complementary Item Recommendation K Bibas, O Sar Shalom, D Jannach Proceedings of the ACM Web Conference 2023, 1804-1812, 2023 | 2 | 2023 |
Collaborative Image Understanding K Bibas, O Sar Shalom, D Jannach Proceedings of the 31st ACM International Conference on Information …, 2022 | 2 | 2022 |
Utilizing adversarial targeted attacks to boost adversarial robustness U Pesso, K Bibas, M Feder arXiv preprint arXiv:2109.01945, 2021 | 2 | 2021 |
Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks S Shayovitz, K Bibas, M Feder Entropy 26 (2), 129, 2024 | | 2024 |
Beyond Ridge Regression for Distribution-Free Data K Bibas, M Feder arXiv preprint arXiv:2206.08757, 2022 | | 2022 |