no code implementations • 7 Apr 2024 • Bishwas Mandal, George Amariucai, Shuangqing Wei
Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner.
no code implementations • 7 Apr 2024 • Bishwas Mandal, George Amariucai, Shuangqing Wei
Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party.
no code implementations • 23 May 2022 • Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang
In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 4 May 2022 • Bishwas Mandal, George Amariucai, Shuangqing Wei
We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware.
no code implementations • 29 Sep 2021 • Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang
In multi-agent deep reinforcement learning, extracting sufficient and compact information of other agents is critical to attain efficient convergence and scalability of an algorithm.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 29 Sep 2021 • Bishwas Mandal, George Amariucai, Shuangqing Wei
The dynamic setting corresponds to the min-max two-player game whereas the constant setting corresponds to a generator which tries to outperform an adversary already trained using ground truth data.
no code implementations • 29 Jun 2020 • Xiaoliang Wan, Shuangqing Wei
VAE is used as a dimension reduction technique to capture the latent space, and KRnet is used to model the distribution of the latent variable.
no code implementations • 26 Jan 2020 • Md Mahmudul Hasan, Shuangqing Wei, Ramachandran Vaidyanathan
Reliable and efficient estimation of the size of a dynamically changing cluster in an IoT network is critical in its nominal operation.
1 code implementation • 8 Jan 2020 • Md Mahmudul Hasan, Shuangqing Wei, Ali Moharrer
We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA), when the population covariance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology.
no code implementations • 23 Jan 2019 • Xiaoliang Wan, Shuangqing Wei
An effective technique to reduce the variance reduction is importance sampling, where we employ the generative model to estimate the distribution of the data from the reduced-order model and use it for the change of measure in the importance sampling estimator.
no code implementations • 21 Jan 2017 • Md Mahmudul Hasan, Shuangqing Wei, Ali Moharrer
The Constrained Minimum Determinant Factor Analysis (CMDFA) setting was motivated by Wyner's common information problem where we seek a latent representation of a given Gaussian vector distribution with the minimum mutual information under certain generative constraints.
Information Theory Information Theory
no code implementations • 24 Jan 2016 • Ali Moharrer, Shuangqing Wei, George T. Amariucai, Jing Deng
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable.