Zobrazeno 1 - 10
of 518
pro vyhledávání: '"Ying, Lexing"'
Eigenvalue transformations, which include solving time-dependent differential equations as a special case, have a wide range of applications in scientific and engineering computation. While quantum algorithms for singular value transformations are we
Externí odkaz:
http://arxiv.org/abs/2411.04010
In LLM alignment and many other ML applications, one often faces the Multi-Objective Fine-Tuning (MOFT) problem, i.e. fine-tuning an existing model with datasets labeled w.r.t. different objectives simultaneously. To address the challenge, we propose
Externí odkaz:
http://arxiv.org/abs/2410.08316
Autor:
Ni, Hongkang, Ying, Lexing
This paper presents two efficient and stable algorithms for recovering phase factors in quantum signal processing (QSP), a crucial component of many quantum algorithms. The first algorithm, the ``Half Cholesky" method, which is based on nonlinear Fou
Externí odkaz:
http://arxiv.org/abs/2410.06409
Discrete diffusion models have gained increasing attention for their ability to model complex distributions with tractable sampling and inference. However, the error analysis for discrete diffusion models remains less well-understood. In this work, w
Externí odkaz:
http://arxiv.org/abs/2410.03601
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 Lindblad dynamics by decomposing its generator into an ensemble of Lindbladians, $\mathcal{L} = \sum_{a \in \mathcal{A}} \mathcal{L}_a$, where each $\mathcal{L}_a$ involves only a single jump opera
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