2 citations · 3 across the 8 of their papers we have counts for
8 papers
Lower Bounds for Anytime Acceleration of Gradient Descent
Chung-En Tsai, Ilyas Fatkhullin, Liang Zhang +1
Recent work suggests that the convergence rate of gradient descent (GD) in smooth convex optimization can be significantly improved by employing large stepsizes that may violate th…
Linear Convergence in Hilbert's Projective Metric for Computing Augustin Information and a Rényi Information Measure
Chung-En Tsai, Guan-Ren Wang, Hao-Chung Cheng +1
Consider the problems of computing the Augustin information and a Rényi information measure of statistical independence, previously explored by Lapidoth and Pfister (IEEE Informati…
On the Synchronization Analysis of a Strong Competition Kuramoto Model
Chun-Hsiung Hsia, Chung-En Tsai
When modeling the classical Kuramoto model, one of the key features is the tendency to synchronize. Accordingly, the most well-adopted choice of the coupling function is the sine f…
Computing Augustin Information via Hybrid Geodesically Convex Optimization
Guan-Ren Wang, Chung-En Tsai, Hao-Chung Cheng +1
We propose a Riemannian gradient descent with the Poincaré metric to compute the order- Augustin information, a widely used quantity for characterizing exponential error behavio…
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
Consider the problem of minimizing an expected logarithmic loss over either the probability simplex or the set of quantum density matrices. This problem includes tasks such as solv…
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…
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…