5 papers
SADA: Safe and Adaptive Aggregation of Multiple Black-Box Predictions in Semi-Supervised Learning
Jiawei Shan, Zhifeng Chen, Yiming Dong +2
Semi-supervised learning (SSL) arises in practice when labeled data are scarce or expensive to obtain, while large quantities of unlabeled data are readily available. With the grow…
Harnessing Bayesian Statistics to Accelerate Iterative Quantum Amplitude Estimation
Qilin Li, Atharva Vidwans, Yazhen Wang +1
We establish a unified statistical framework that underscores the crucial role statistical inference plays in Quantum Amplitude Estimation (QAE), a task essential to fields ranging…
Learning Relationship between Quantum Walks and Underdamped Langevin Dynamics
Yazhen Wang
Fast computational algorithms are in constant demand, and their development has been driven by advances such as quantum speedup and classical acceleration. This paper intends to st…
Robust Reinforcement Learning under Diffusion Models for Data with Jumps
Chenyang Jiang, Donggyu Kim, Alejandra Quintos +1
Reinforcement Learning (RL) has proven effective in solving complex decision-making tasks across various domains, but challenges remain in continuous-time settings, particularly wh…
Computational and Statistical Asymptotic Analysis of the JKO Scheme for Iterative Algorithms to update distributions
Shang Wu, Yazhen Wang
The seminal paper of Jordan, Kinderlehrer, and Otto introduced what is now widely known as the JKO scheme, an iterative algorithmic framework for computing distributions. This sche…