AbstractThe paper introduces a way of re-constructing a loss function from predictive complexity. We show that a loss function and expectations of the corresponding predictive complexity w.r.t. the Bernoulli distribution are related through the Legendre transformation. It is shown that if two loss functions specify the same complexity then they are equivalent in a strong sense. The expectations are also related to the so-called generalized entropy
The paper explores connections between asymptotic complexity and generalised entropy. Asymptotic com...
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e...
AbstractWe bound the future loss when predicting any (computably) stochastic sequence online. Solomo...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
AbstractThe notions of predictive complexity and of corresponding amount of information are consider...
AbstractIn this paper we introduce a general method of establishing tight linear inequalities betwee...
AbstractIn this paper we introduce a general method of establishing tight linear inequalities betwee...
AbstractPredictive complexity is a generalization of Kolmogorov complexity. It corresponds to an “op...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
AbstractThe central problem in machine learning (and statistics) is the problem of predicting future...
AbstractThe usual theory of prediction with expert advice does not differentiate between good and ba...
AbstractThis paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-logm,...
The paper explores connections between asymptotic complexity and generalised entropy. Asymptotic com...
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e...
AbstractWe bound the future loss when predicting any (computably) stochastic sequence online. Solomo...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
The paper introduces a way of re-constructing a loss function from predictive complexity. We show t...
AbstractThe notions of predictive complexity and of corresponding amount of information are consider...
AbstractIn this paper we introduce a general method of establishing tight linear inequalities betwee...
AbstractIn this paper we introduce a general method of establishing tight linear inequalities betwee...
AbstractPredictive complexity is a generalization of Kolmogorov complexity. It corresponds to an “op...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
We bound the future loss when predicting any (computably) stochastic sequence online. Solomonoff fin...
AbstractThe central problem in machine learning (and statistics) is the problem of predicting future...
AbstractThe usual theory of prediction with expert advice does not differentiate between good and ba...
AbstractThis paper studies sequence prediction based on the monotone Kolmogorov complexity Km=-logm,...
The paper explores connections between asymptotic complexity and generalised entropy. Asymptotic com...
This paper studies sequence prediction based on the monotone Kolmogorov complexity Km = − log m, i.e...
AbstractWe bound the future loss when predicting any (computably) stochastic sequence online. Solomo...