Fanny Yang

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This paper considers the recovery of continuous signals in infinite dimensional spaces from the magnitude of its frequency samples. It proposes a sampling scheme which involves a combination of oversampling and specific structured modulations. This allows for almost every signal with finite support to be reconstructed from its magnitude samples in the(More)
This paper considers the recovery of continuous time signals from the magnitude of its samples. It uses a combination of structured modulation and oversampling and provides sufficient conditions on the signal and the sampling system such that signal recovery is possible. In particular, it is shown that an average sampling rate of four times the Nyquist rate(More)
The paper considers the phase retrieval problem in N dimensional complex vector spaces. It provides two sets of deterministic measurement vectors which guarantee signal recovery for all signals, excluding only a specific subspace and a union of subspaces, respectively. A stable analytic reconstruction procedure of low complexity is given. Additionally it is(More)
This paper considers the problem of signal recovery from magnitude measurements for signals in modulation invariant spaces. It proposes a measurement setup such that almost every signal in such a signal space can be reconstructed from its amplitude measurements up to a global constant phase and with a sampling rate of four times the rate of innovation of(More)
We propose a an alternative framework to existing setups for controlling false alarms when multiple A/B tests are run over time. This setup arises in many practical applications, e.g. when pharmaceutical companies test new treatment options against control pills for different diseases, or when internet companies test their default webpages versus various(More)
Early stopping of iterative algorithms is a widely-used form of regularization in statistics, commonly used in conjunction with boosting and related gradient-type algorithms. Although consistency results have been established in some settings, such estimators are less well-understood than their analogues based on penalized regularization. In this paper, for(More)
The paper considers the phase retrieval problem in N dimensional complex vector spaces. It provides two sets of deterministic measurement vectors which guarantee signal recovery for all signals, excluding only a specific subspace and a union of subspaces, respectively. A stable analytic reconstruction procedure of low complexity is given. Additionally it is(More)
This paper derives causal reconstruction kernels which allow for a consistent signal recovery of the past signal component from the past signal samples only. Our approach is based on classical Hilbert space methods of signal sampling and recovery. The causal reconstruction kernels are obtained as the causal dual frame for a given sequence of sampling(More)
The Hidden Markov Model (HMM) is one of the main-stays of statistical modeling of discrete time series and is widely used in many applications. Estimating an HMM from its observation process is often addressed via the Baum-Welch algorithm, which performs well empirically when initialized reasonably close to the truth. This behavior could not be explained by(More)
In the online multiple testing problem, p-values corresponding to different null hypotheses are observed one by one, and the decision of whether or not to reject the current hypothesis must be made immediately, after which the next p-value is observed. Alpha-investing algorithms to control the false discovery rate (FDR), formulated by Foster and Stine, have(More)