Koby Bibas

According to our database, Koby Bibas authored at least 11 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Deep Individual Active Learning: Safeguarding against Out-of-Distribution Challenges in Neural Networks.

Shachar Shayovitz, Koby Bibas, Meir Feder

Entropy, February, 2024

2023

Semi-supervised Adversarial Learning for Complementary Item Recommendation.

Koby Bibas, Oren Sar Shalom, Dietmar Jannach

Proceedings of the ACM Web Conference 2023, 2023

2022

Beyond Ridge Regression for Distribution-Free Data.

Koby Bibas, Meir Feder

CoRR, 2022

Collaborative Image Understanding.

Koby Bibas, Oren Sar Shalom, Dietmar Jannach

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness.

Uriya Pesso, Koby Bibas, Meir Feder

CoRR, 2021

The Predictive Normalized Maximum Likelihood for Over-parameterized Linear Regression with Norm Constraint: Regret and Double Descent.

Koby Bibas, Meir Feder

CoRR, 2021

Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection.

Koby Bibas, Meir Feder, Tal Hassner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Rotation Invariant Features For Cryogenic Electron Microscopy Image Reconstruction.

Koby Bibas, Gili Weiss-Dicker, Dana Cohen, Noa Cahan, Hayit Greenspan

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

2019

Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks.

Koby Bibas, Yaniv Fogel, Meir Feder

CoRR, 2019

A New Look at an Old Problem: A Universal Learning Approach to Linear Regression.

Koby Bibas, Yaniv Fogel, Meir Feder

Proceedings of the IEEE International Symposium on Information Theory, 2019

Balancing Specialization, Generalization, and Compression for Detection and Tracking.

Dotan Kaufman, Koby Bibas, Eran Borenstein, Michael Chertok, Tal Hassner

Proceedings of the 30th British Machine Vision Conference 2019, 2019