Gilad Yehudai

Publications by Year

Research Areas

Stochastic Gradient Optimization Techniques, Neural Networks and Applications, Adversarial Robustness in Machine Learning, Machine Learning and Algorithms, Domain Adaptation and Few-Shot Learning

Most-Cited Works

• → Proving the Lottery Ticket Hypothesis: Pruning is All You Need(2020)73 cited
• → Reconstructing Training Data from Trained Neural Networks(2022)29 cited
• → On the Power and Limitations of Random Features for Understanding Neural\n Networks(2019)29 cited
• → Learning a Single Neuron with Gradient Methods(2020)11 cited
• → From Local Structures to Size Generalization in Graph Neural Networks(2020)9 cited
• → The Effects of Mild Over-parameterization on the Optimization Landscape of Shallow ReLU Neural Networks(2020)6 cited
• → Gradient Methods Provably Converge to Non-Robust Networks(2022)6 cited
• → The Connection Between Approximation, Depth Separation and Learnability in Neural Networks(2021)4 cited