activity
20202025
most citedImproving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

quant-ph2025

On Estimating the Quantum Tsallis Relative Entropy

Jinge Bao, Minbo Gao, Qisheng Wang

The relative entropy between quantum states quantifies their distinguishability. The estimation of certain relative entropies has been investigated in the literature, e.g., the von…

quant-ph2025

Information-Theoretic Lower Bounds for Approximating Monomials via Optimal Quantum Tsallis Entropy Estimation

Qisheng Wang

This paper reveals a conceptually new connection from information theory to approximation theory via quantum algorithms for entropy estimation. Specifically, we provide an informat…

quant-ph2024

Quantum Approximate -Minimum Finding

Minbo Gao, Zhengfeng Ji, Qisheng Wang

Quantum -minimum finding is a fundamental subroutine with numerous applications in combinatorial problems and machine learning. Previous approaches typically assume oracle acces…

cs.LG2024

Quantum Algorithm for Sparse Online Learning with Truncated Gradient Descent

Debbie Lim, Yixian Qiu, Patrick Rebentrost +1

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied methods in the statistical and computer science community, with various practical applicat…

cs.LG20207 cited

Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets

Liwei Jiang, Dan Li, Qisheng Wang +2

Positive Unlabeled (PU) learning is widely used in many applications, where a binary classifier is trained on the datasets consisting of only positive and unlabeled samples. In thi…