6 citations · 19 across the 12 of their papers we have counts for
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Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift
Teddy Lazebnik
Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientif…
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models
Assaf Shmuel, Teddy Lazebnik, Oren Glickman +2
Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires…
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
The analysis of tabular datasets is highly prevalent both in scientific research and real-world applications of Machine Learning (ML). Unlike many other ML tasks, Deep Learning (DL…
Introducing 'Inside' Out of Distribution
Teddy Lazebnik
Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies primarily focus on extra…
Evaluating Supply Chain Resilience During Pandemic Using Agent-based Simulation
Teddy Lazebnik
Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. Possible future pandemic raises a dilemma for businesses owners between…
Transforming Norm-based To Graph-based Spatial Representation for Spatio-Temporal Epidemiological Models
Teddy Lazebnik
Pandemics, with their profound societal and economic impacts, pose significant threats to global health, mortality rates, economic stability, and political landscapes. In response…