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Found 9 papers, 0 papers with code
Machine Learning Prescriptive Canvas for Optimizing Business Outcomes
Data science has the potential to improve business in a variety of verticals.
Positivity Validation Detection and Explainability via Zero Fraction Multi-Hypothesis Testing and Asymmetrically Pruned Decision Trees
Positivity is one of the three conditions for causal inference from observational data.
DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
Dynamic difficulty adjustment ($DDA$) is a process of automatically changing a game difficulty for the optimization of user experience.
Generalized Quantile Loss for Deep Neural Networks
This note presents a simple way to add a count (or quantile) constraint to a regression neural net, such that given $n$ samples in the training set it guarantees that the prediction of $m<n$ samples will be larger than the actual value (the label).
Majority Voting and the Condorcet's Jury Theorem
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 to choose correctly when individual votes are often correct and independent, and a modern Machine Learning concept called "Strength of Weak Learnability" (1990), which describes a method for converting a weak learning algorithm into one that achieves arbitrarily high accuracy and stands in the basis of Ensemble Learning.
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning
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 condition number substantially, but also efficient in its application in terms of computational cost and memory consumption.
Similarity Search Over Graphs Using Localized Spectral Analysis
This paper provides a new similarity detection algorithm.
Randomized LU decomposition: An Algorithm for Dictionaries Construction
Such approach is harder to deceive and we show that only a few file fragments from a whole file are needed for a successful classification.
Missing Entries Matrix Approximation and Completion
The approximation constraint can be any whose approximated solution is known for the full matrix.
