Search Results for author: Yen-Huan Li

Found 9 papers, 2 papers with code

Online Learning Quantum States with the Logarithmic Loss via VB-FTRL

no code implementations6 Nov 2023 Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li

Online learning quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection, a classic open problem in the field of online learning for over three decades.

Quantum State Tomography

Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging

2 code implementations5 Nov 2023 Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

For the Poisson inverse problem, our algorithm attains an $\varepsilon$-optimal solution in $\smash{\tilde{O}}(d^2/\varepsilon^2)$ time, matching the state of the art, where $d$ denotes the dimension.

Quantum State Tomography

Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography

1 code implementation23 Nov 2022 Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits.

Quantum State Tomography

Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States

no code implementations3 Oct 2022 Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$.

Maximum-Likelihood Quantum State Tomography by Soft-Bayes

no code implementations31 Dec 2020 Chien-Ming Lin, Yu-Ming Hsu, Yen-Huan Li

Quantum state tomography (QST), the task of estimating an unknown quantum state given measurement outcomes, is essential to building reliable quantum computing devices.

Learning Theory Quantum State Tomography

Learning-Based Compressive MRI

no code implementations3 May 2018 Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li, Efe Ilıcak, Tolga Çukur, Jonathan Scarlett, Volkan Cevher

In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns.

Anatomy Learning Theory

Learning Data Triage: Linear Decoding Works for Compressive MRI

no code implementations1 Feb 2016 Yen-Huan Li, Volkan Cevher

The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure.

Learning-based Compressive Subsampling

no code implementations21 Oct 2015 Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett, Baran Gözcü, Ilija Bogunovic, Volkan Cevher

In this paper, we instead take a principled learning-based approach in which a \emph{fixed} index set is chosen based on a set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$.

Combinatorial Optimization

Composite convex minimization involving self-concordant-like cost functions

no code implementations4 Feb 2015 Quoc Tran-Dinh, Yen-Huan Li, Volkan Cevher

The self-concordant-like property of a smooth convex function is a new analytical structure that generalizes the self-concordant notion.

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