Zobrazeno 1 - 10
of 509
pro vyhledávání: '"YING, LEXING"'
This work proposes a novel numerical scheme for solving the high-dimensional Hamilton-Jacobi-Bellman equation with a functional hierarchical tensor ansatz. We consider the setting of stochastic control, whereby one applies control to a particle under
Externí odkaz:
http://arxiv.org/abs/2408.04209
We study a qDRIFT-type randomized method to simulate the Lindblad equations. For Lindblad dynamics generated by an ensemble of Lindbladians $\{\mathcal{L}_a\}_{a \in \mathcal{A}}$, our approach implements a single randomly sampled Lindbladian $\mathc
Externí odkaz:
http://arxiv.org/abs/2407.06594
Autor:
Ying, Lexing
Differential privacy is a framework for protecting the identity of individual data points in the decision-making process. In this note, we propose a new form of differential privacy called tangent differential privacy. Compared with the usual differe
Externí odkaz:
http://arxiv.org/abs/2406.04535
Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and evaluate, reducing the inference cost for diffusion models remains a major goal. Inspired by the recent e
Externí odkaz:
http://arxiv.org/abs/2405.15986
Autor:
Ying, Lexing
In online learning, the data is provided in a sequential order, and the goal of the learner is to make online decisions to minimize overall regrets. This note is concerned with continuous-time models and algorithms for several online learning problem
Externí odkaz:
http://arxiv.org/abs/2405.10399
Autor:
Ni, Hongkang, Ying, Lexing
Different kinds of wave packet transforms are widely used for extracting multi-scale structures in signal processing tasks. This paper introduces the quantum circuit implementation of a broad class of wave packets, including Gabor atoms and wavelets,
Externí odkaz:
http://arxiv.org/abs/2405.00929
Autor:
Ying, Lexing
Spectral estimation is a fundamental task in signal processing. Recent algorithms in quantum phase estimation are concerned with the large noise, large frequency regime of the spectral estimation problem. The recent work in Ding-Epperly-Lin-Zhang sho
Externí odkaz:
http://arxiv.org/abs/2405.00803
Bootstrap is a popular methodology for simulating input uncertainty. However, it can be computationally expensive when the number of samples is large. We propose a new approach called \textbf{Orthogonal Bootstrap} that reduces the number of required
Externí odkaz:
http://arxiv.org/abs/2404.19145
Solving high-dimensional Kolmogorov backward equations with functional hierarchical tensor operators
Solving high-dimensional partial differential equations necessitates methods free of exponential scaling in the dimension of the problem. This work introduces a tensor network approach for the Kolmogorov backward equation via approximating directly t
Externí odkaz:
http://arxiv.org/abs/2404.08823
Autor:
Tang, Xun, Rahmanian, Holakou, Shavlovsky, Michael, Thekumparampil, Kiran Koshy, Xiao, Tesi, Ying, Lexing
Entropic optimal transport (OT) and the Sinkhorn algorithm have made it practical for machine learning practitioners to perform the fundamental task of calculating transport distance between statistical distributions. In this work, we focus on a gene
Externí odkaz:
http://arxiv.org/abs/2403.05054