6 citations · 19 across the 12 of their papers we have counts for
12 papers
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
Osnat Mokryn, Teddy Lazebnik, Hagit Ben Shoshan
The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medica…
Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method
Uri Itai, Asael Bar Ilan, Teddy Lazebnik
Detecting out-of-distribution (OOD) data is a critical task for maintaining model reliability and robustness. In this study, we propose a novel anomaly detection algorithm that lev…
An Empirically-parametrized Spatio-Temporal Extended-SIR Model for Combined Dilution and Vaccination Mitigation for Rabies Outbreaks in Wild Jackals
Teddy Lazebnik, Yehuda Samuel, Jonathan Tichon +4
The transmission of zoonotic diseases between animals and humans poses an increasing threat. Rabies is a prominent example with various instances globally, facilitated by a surplus…
Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Deep learning (DL) models have gained prominence in domains such as computer vision and natural language processing but remain underutilized for regression tasks involving tabular…
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…
Individual Variation Affects Outbreak Magnitude and Predictability in an Extended Multi-Pathogen SIR Model of Pigeons Vising Dairy Farms
Teddy Lazebnik, Orr Spiegel
Zoonotic disease transmission between animals and humans is a growing risk and the agricultural context acts as a likely point of transition, with individual heterogeneity acting a…
Rivendell: Project-Based Academic Search Engine
Teddy Lazebnik, Hanna Weitman, Yoav Goldberg +1
Finding relevant research literature in online databases is a familiar challenge to all researchers. General search approaches trying to tackle this challenge fall into two groups:…