3 papers
cs.LG2026
Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression
Gilad Karpel, Daniel Soudry
We study continual linear regression in a high-dimensional regime, where a learner observes a sequence of tasks and updates its parameters sequentially. In this setting, we derive…
cs.LG2026
Optimal L2 Regularization in High-dimensional Continual Linear Regression
Gilad Karpel, Edward Moroshko, Ran Levinstein +3
We study generalization in an overparameterized continual linear regression setting, where a model is trained with L2 (isotropic) regularization across a sequence of tasks. We deri…
cs.LG2025
Directional-Clamp PPO
Gilad Karpel, Ruida Zhou, Shoham Sabach +1
Proximal Policy Optimization (PPO) is widely regarded as one of the most successful deep reinforcement learning algorithms, known for its robustness and effectiveness across a rang…