collaborators

5 papers

stat.AP2025

Chronic Stress, Immune Suppression, and Cancer Occurrence: Unveiling the Connection using Survey Data and Predictive Models

Teddy Lazebnik, Vered Aharonson

Chronic stress was implicated in cancer occurrence, but a direct causal connection has not been consistently established. Machine learning and causal modeling offer opportunities t…

cs.LG2025

Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing

Lukas Toral, Teddy Lazebnik

Reinforcement Learning (RL) algorithms often require long training to become useful, especially in complex environments with sparse rewards. While techniques like reward shaping an…

q-bio.QM2024

Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Healthcare System Administration using Deep Reinforcement Learning

Adi Shuchami, Teddy Lazebnik

Large-scale crises, including wars and pandemics, have repeatedly shaped human history, and their simultaneous occurrence presents profound challenges to societies. Understanding t…

cs.SI2024

Mathematical model of dating apps influence on sexually transmitted diseases spread

Teddy Lazebnik

Sexually transmitted diseases (STDs) are a group of pathogens infecting new hosts through sexual interactions. Due to its social and economic burden, multiple models have been prop…

cs.CV2024

Break a Lag: Triple Exponential Moving Average for Enhanced Optimization

Roi Peleg, Yair Smadar, Teddy Lazebnik +1

The performance of deep learning models is critically dependent on sophisticated optimization strategies. While existing optimizers have shown promising results, many rely on first…