240 citations · 503 across the 31 of their papers we have counts for
17 papers · 1 filter
Active Learning with Label Comparisons
Gal Yona, Shay Moran, Gal Elidan +1
Supervised learning typically relies on manual annotation of the true labels. When there are many potential classes, searching for the best one can be prohibitive for a human annot…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Solving Sokoban with forward-backward reinforcement learning
Yaron Shoham, Gal Elidan
Despite seminal advances in reinforcement learning in recent years, many domains where the rewards are sparse, e.g. given only at task completion, remain quite challenging. In such…
HydroNets: Leveraging River Structure for Hydrologic Modeling
Zach Moshe, Asher Metzger, Gal Elidan +3
Accurate and scalable hydrologic models are essential building blocks of several important applications, from water resource management to timely flood warnings. However, as the cl…
DNF-Net: A Neural Architecture for Tabular Data
Ami Abutbul, Gal Elidan, Liran Katzir +1
A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is…
MadNet: Using a MAD Optimization for Defending Against Adversarial Attacks
Shai Rozenberg, Gal Elidan, Ran El-Yaniv
This paper is concerned with the defense of deep models against adversarial attacks. Inspired by the certificate defense approach, we propose a maximal adversarial distortion (MAD)…
Spectral Algorithm for Low-rank Multitask Regression
Yotam Gigi, Ami Wiesel, Sella Nevo +3
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learnin…
ML for Flood Forecasting at Scale
Sella Nevo, Vova Anisimov, Gal Elidan +11
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount o…
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Yotam Gigi, Gal Elidan, Avinatan Hassidim +5
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river disc…
Learning Rules-First Classifiers
Deborah Cohen, Amit Daniely, Amir Globerson +1
Complex classifiers may exhibit "embarassing" failures in cases where humans can easily provide a justified classification. Avoiding such failures is obviously of key importance. I…
Speedy Model Selection (SMS) for Copula Models
Yaniv Tenzer, Gal Elidan
We tackle the challenge of efficiently learning the structure of expressive multivariate real-valued densities of copula graphical models. We start by theoretically substantiating…
Learning Max-Margin Tree Predictors
Ofer Meshi, Elad Eban, Gal Elidan +1
Structured prediction is a powerful framework for coping with joint prediction of interacting outputs. A central difficulty in using this framework is that often the correct label…
Learning the Dimensionality of Hidden Variables
Gal Elidan, Nir Friedman
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detec…
The Information Bottleneck EM Algorithm
Gal Elidan, Nir Friedman
Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using…
"Ideal Parent" Structure Learning for Continuous Variable Networks
Iftach Nachman, Gal Elidan, Nir Friedman
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedu…
Convex Point Estimation using Undirected Bayesian Transfer Hierarchies
Gal Elidan, Ben Packer, Geremy Heitz +1
When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bay…
Inference-less Density Estimation using Copula Bayesian Networks
Gal Elidan
We consider learning continuous probabilistic graphical models in the face of missing data. For non-Gaussian models, learning the parameters and structure of such models depends on…