Adaptive Hammerstein Filtering via Recursive Non-Convex Projection

Liu, Zhaoting; Li, Chunguang

doi:10.1109/tsp.2022.3180195

Cited by 10 publications

(2 citation statements)

References 41 publications

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“…where W −1 is the Lambert-W function, u is the uniform distribution with (u) m = 1/M for all m, and d t is learned using the truncated exponential weighting in Corollary V. 3.…”

Section: Exponential Weighting With Truncationmentioning

confidence: 99%

“…'expert advice') in real-time, for various prediction tasks in numerous fields of science and engineering. Some examples of prediction tasks pertaining to signal processing and system automation include: adaptive filtering [2], [3], predictive control [4], feature extraction [5], [6], parameter estimation [7], [8], non-convex optimization [9], [10], classification [11]- [14], time-series analysis [15], speech de-reverberation [16].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Tracking in Prediction with Expert Advice

Gokcesu¹,

Kozat²

2022

Preprint

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We study the prediction with expert advice setting, where the aim is to produce a decision by combining the decisions generated by a set of experts, e.g., independently running algorithms. We achieve the min-max optimal dynamic regret under the prediction with expert advice setting, i.e., we can compete against time-varying (not necessarily fixed) combinations of expert decisions in an optimal manner. Our end-algorithm is truly online with no prior information, such as the time horizon or loss ranges, which are commonly used by different algorithms in the literature. Both our regret guarantees and the min-max lower bounds are derived with the general consideration that the expert losses can have time-varying properties and are possibly unbounded. Our algorithm can be adapted for restrictive scenarios regarding both loss feedback and decision making. Our guarantees are universal, i.e., our end-algorithm can provide regret guarantee against any competitor sequence in a min-max optimal manner with logarithmic complexity. Note that, to our knowledge, for the prediction with expert advice problem, our algorithms are the first to produce such universally optimal, adaptive and truly online guarantees with no prior knowledge.This study is partially supported by Turkcell Technology within the framework of 5G and Beyond Joint Graduate Support Programme coordinated by Information and Communication Technologies Authority.

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“…where W −1 is the Lambert-W function, u is the uniform distribution with (u) m = 1/M for all m, and d t is learned using the truncated exponential weighting in Corollary V. 3.…”

Section: Exponential Weighting With Truncationmentioning

confidence: 99%