Vitaly Kuznetsov

Generalization Bounds for Time Series Prediction with Non-stationary Processes

This paper presents the first generalization bounds for time series prediction with a non-stationary mixing stochastic process. We prove Rademacher complexity learning bounds for both average-path generalization with non-stationary β-mixing processes and path-dependent generalization with non-stationary ϕ-mixing processes. Our guarantees are expressed in terms of β- or ϕ-mixing coefficients and a natural measure of discrepancy between training and target distributions. They admit as special cases previous Rademacher complexity bounds for non-i.i.d. stationary distributions, for independent but not identically distributed random variables, or for the i.i.d. case. We show that, using a new sub-sample selection technique we introduce, our bounds can be tightened under the natural assumption of convergent stochastic processes. We also prove that fast learning rates can be achieved by extending existing local Rademacher complexity analysis to non-i.i.d. setting.

Generalization bounds for non-stationary mixing processes

This paper presents the first generalization bounds for time series prediction with a non-stationary mixing stochastic process. We prove Rademacher complexity learning bounds for both average-path generalization with non-stationary

Learning N-gram language models from uncertain data

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Nested recursions with ceiling function solutions

β-mixing processes and path-dependent generalization with non-stationary

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

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Ensemble Methods for Structured Prediction

ϕ-mixing processes. Our guarantees are expressed in terms of