27 citations · 62 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
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…