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Found 5 papers, 0 papers with code
Prediction of Locally Stationary Data Using Expert Advice
The problem of continuous machine learning is studied.
Online Aggregation of Probability Forecasts with Confidence
In this paper the problem of combining probabilistic forecasts is considered in the PEA framework.
Online Learning with Continuous Ranked Probability Score
Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields of statistical science.
Online Aggregation of Unbounded Losses Using Shifting Experts with Confidence
We develop the setting of sequential prediction based on shifting experts and on a "smooth" version of the method of specialized experts.
Universal Algorithm for Online Trading Based on the Method of Calibration
We present a universal algorithm for online trading in Stock Market which performs asymptotically at least as good as any stationary trading strategy that computes the investment at each step using a fixed function of the side information that belongs to a given RKHS (Reproducing Kernel Hilbert Space).
