We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff finitely bounded the total deviation of his universal predictor M from the true dis...
The neural network (NN) models well trained and validated by the same data may exhibit noticeably different predictabilities in applications. This is mainly due to the fact that t...
We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three criteria to expand a node in TD...
We apply speculative multithreading to sequential Java programs in software to achieve speedup on existing multiprocessors. A common speculation library supports both Java bytecod...
Prediction from expert advice is a fundamental problem in machine learning. A major pillar of the field is the existence of learning algorithms whose average loss approaches that ...