27 citations · 62 across the 15 of their papers we have counts for
17 papers
Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting
Ron Begleiter, Katya Egert Berg, Gilad Saban +1
Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industrial settings. While monolithi…
The Geometry of the Pivot: A Note on Lazy Pivoted Cholesky and Farthest Point Sampling
Gil Shabat
Low-rank approximations of large kernel matrices are ubiquitous in machine learning, particularly for scaling Gaussian Processes to massive datasets. The Pivoted Cholesky decomposi…
Machine Learning Prescriptive Canvas for Optimizing Business Outcomes
Hanan Shteingart, Gerben Oostra, Ohad Levinkron +3
Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements thes…
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees
Guy Wolf, Gil Shabat, Hanan Shteingart
Positivity is one of the three conditions for causal inference from observational data. The standard way to validate positivity is to analyze the distribution of propensity. Howeve…
DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
Dvir Ben Or, Michael Kolomenkin, Gil Shabat
Dynamic difficulty adjustment () is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game…
Generalized Quantile Loss for Deep Neural Networks
Dvir Ben Or, Michael Kolomenkin, Gil Shabat
This note presents a simple way to add a count (or quantile) constraint to a regression neural net, such that given samples in the training set it guarantees that the predictio…
Super-resolution SAXS based on PSF engineering and sub-pixel detector translations
Benjamin Gutman, Michael Mrejen, Gil Shabat +3
Small-angle X-ray scattering (SAXS) technique enables convenient nanoscopic characterization for various systems and conditions. Nonetheless, lab-based SAXS systems intrinsically s…
Majority Voting and the Condorcet's Jury Theorem
Hanan Shteingart, Eran Marom, Igor Itkin +4
There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely t…
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning
Gil Shabat, Era Choshen, Dvir Ben Or +1
This paper presents a method for building a preconditioner for a kernel ridge regression problem, where the preconditioner is not only effective in its ability to reduce the condit…
Similarity Search Over Graphs Using Localized Spectral Analysis
Yariv Aizenbud, Amir Averbuch, Gil Shabat +1
This paper provides a new similarity detection algorithm. Given an input set of multi-dimensional data points, where each data point is assumed to be multi-dimensional, and an addi…
Randomized LU Decomposition Using Sparse Projections
Yariv Aizenbud, Gil Shabat, Amir Averbuch
A fast algorithm for the approximation of a low rank LU decomposition is presented. In order to achieve a low complexity, the algorithm uses sparse random projections combined with…
Direct Inversion of the 3D Pseudo-polar Fourier Transform
Amir Averbuch, Gil Shabat, Yoel Shkolnisky
The pseudo-polar Fourier transform is a specialized non-equally spaced Fourier transform, which evaluates the Fourier transform on a near-polar grid, known as the pseudo-polar grid…
Randomized LU decomposition: An Algorithm for Dictionaries Construction
Aviv Rotbart, Gil Shabat, Yaniv Shmueli +1
In recent years, distinctive-dictionary construction has gained importance due to his usefulness in data processing. Usually, one or more dictionaries are constructed from a traini…
Randomized LU Decomposition
Gil Shabat, Yaniv Shmueli, Yariv Aizenbud +1
We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses random projections type techniques to efficiently compute a low rank approximat…
Missing Entries Matrix Approximation and Completion
Gil Shabat, Yaniv Shmueli, Amir Averbuch
We describe several algorithms for matrix completion and matrix approximation when only some of its entries are known. The approximation constraint can be any whose approximated so…
Potentials and Limits of Super-Resolution Algorithms and Signal Reconstruction from Sparse Data
Gil Shabat
A common distortion in videos is image instability in the form of chaotic (global and local displacements). Those instabilities can be used to enhance image resolution by using sub…
Non-uniform sampling, image recovery from sparse data and the discrete sampling theorem
Leonid P. Yaroslavsky, Gil Shabat, Benny G. Salomon +2
In many applications sampled data are collected in irregular fashion or are partly lost or unavailable. In these cases it is required to convert irregularly sampled signals to regu…