109 citations · 170 across the 17 of their papers we have counts for
20 papers
Data-Dependent Bounds for Online Portfolio Selection Without Lipschitzness and Smoothness
Chung-En Tsai, Ying-Ting Lin, Yen-Huan Li
This work introduces the first small-loss and gradual-variation regret bounds for online portfolio selection, marking the first instances of data-dependent bounds for online convex…
Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography
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. It is therefore desirable to develop a stochastic f…
Two Polyak-Type Step Sizes for Mirror Descent
Jun-Kai You, Yen-Huan Li
We propose two Polyak-type step sizes for mirror descent and prove their convergences for minimizing convex locally Lipschitz functions. Both step sizes, unlike the original Polyak…
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
Consider an online convex optimization problem where the loss functions are self-concordant barriers, smooth relative to a convex function , and possibly non-Lipschitz. We analy…
Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis
Chien-Ming Lin, Hao-Chung Cheng, Yen-Huan Li
We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( (…
Minimizing Quantum Renyi Divergences via Mirror Descent with Polyak Step Size
Jun-Kai You, Hao-Chung Cheng, Yen-Huan Li
Quantum information quantities play a substantial role in characterizing operational quantities in various quantum information-theoretic problems. We consider numerical computation…
Maximum-Likelihood Quantum State Tomography by Soft-Bayes
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. Whereas com…
A General Convergence Result for Mirror Descent with Armijo Line Search
Yen-Huan Li, Carlos A. Riofrio, Volkan Cevher
Existing convergence guarantees for the mirror descent algorithm require the objective function to have a bounded gradient or be smooth relative to a Legendre function. The bounded…
Learning-Based Compressive MRI
Baran Gözcü, Rabeeh Karimi Mahabadi, Yen-Huan Li +4
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 pat…
Convergence of the Exponentiated Gradient Method with Armijo Line Search
Yen-Huan Li, Volkan Cevher
Consider the problem of minimizing a convex differentiable function on the probability simplex, spectrahedron, or set of quantum density matrices. We prove that the exponentiated g…
A General Convergence Result for the Exponentiated Gradient Method
Yen-Huan Li, Volkan Cevher
The batch exponentiated gradient (EG) method provides a principled approach to convex smooth minimization on the probability simplex or the space of quantum density matrices. Howev…
Learning Data Triage: Linear Decoding Works for Compressive MRI
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…
Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval
Gergely Odor, Yen-Huan Li, Alp Yurtsever +4
We study a phase retrieval problem in the Poisson noise model. Motivated by the PhaseLift approach, we approximate the maximum-likelihood estimator by solving a convex program with…
Learning-based Compressive Subsampling
Luca Baldassarre, Yen-Huan Li, Jonathan Scarlett +3
The problem of recovering a structured signal from a set of dimensionality-reduced linear measurements arises in…
A Geometric View on Constrained M-Estimators
Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib +1
We study the estimation error of constrained M-estimators, and derive explicit upper bounds on the expected estimation error determined by the Gaussian width of the constraint set.…
Composite convex minimization involving self-concordant-like cost functions
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. While a wide variety of important applicati…
Sparsistency of -Regularized -Estimators
Yen-Huan Li, Jonathan Scarlett, Pradeep Ravikumar +1
We consider the model selection consistency or sparsistency of a broad set of -regularized -estimators for linear and non-linear statistical models in a unified fashion.…
A Cramer-Rao Bound for Semi-Blind Channel Estimation in Redundant Block Transmission Systems
Yen-Huan Li, Borching Su, Ping-Cheng Yeh
A Cramer-Rao bound (CRB) for semi-blind channel estimators in redundant block transmission systems is derived. The derived CRB is valid for any system adopting a full-rank linear r…
An Interpretation of the Moore-Penrose Generalized Inverse of a Singular Fisher Information Matrix
Yen-Huan Li, Ping-Cheng Yeh
It is proved that in a non-Bayesian parametric estimation problem, if the Fisher information matrix (FIM) is singular, unbiased estimators for the unknown parameter will not exist.…
Cramer-Rao Bound for Blind Channel Estimators in Redundant Block Transmission Systems
Yen-Huan Li, Borching Su, Ping-Cheng Yeh
In this paper, we derive the Cramer-Rao bound (CRB) for blind channel estimation in redundant block transmission systems, a lower bound for the mean squared error of any blind chan…