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Found 7 papers, 1 papers with code
Automatic Regularization for Linear MMSE Filters
In this work, we consider the problem of regularization in minimum mean-squared error (MMSE) linear filters.
Stochastic analysis of the Elo rating algorithm in round-robin tournaments
The Elo algorithm, renowned for its simplicity, is widely used for rating in sports tournaments and other applications.
Rankability and Linear Ordering Problem: New Probabilistic Insight and Algorithms
), so we reformulate the problem and introduce a concept of the Slater spectrum that generalizes the Slater index, and then devise an algorithm to find the spectrum with complexity O(M^3 2^M) that is manageable for moderate values of M. Furthermore, with a minor modification of the algorithm, we are able to find all solutions of the LOP with the complexity O(M 2^M).
FIFA ranking: Evaluation and path forward
In particular, analyzing the games since the introduction of the algorithm in 2018, we conclude that the game's "importance" (as defined by FIFA) used in the algorithm is counterproductive from the point of view of the predictive capability of the algorithm.
Simplified Kalman filter for online rating: one-fits-all approach
In order to clarify the conditions under which the gains of the Bayesian approach over the simpler solutions can actually materialize, we critically compare the known and the new algorithms by means of numerical examples using the synthetic as well as the empirical data.
G-Elo: Generalization of the Elo algorithm by modelling the discretized margin of victory
Third, we propose a simple method to estimate the coefficients of the model, and thus define the operation of the algorithm; it is done in a closed form using the historical data so the algorithm is tailored to the sport of interest and the coefficients defining its operation are determined in entirely transparent manner.
Bilinear Models for Machine Learning
In this work we define and analyze the bilinear models which replace the conventional linear operation used in many building blocks of machine learning (ML).
