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Found 12 papers, 2 papers with code
Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
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.
Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
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.
Learning to Advise and Learning from Advice in Cooperative Multi-Agent Reinforcement Learning
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
Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation
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.
UAE-PUPET: An Uncertainty-Autoencoder-Based Privacy and Utility Preserving End-to-End Transformation
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.
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning
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
VAE-KRnet and its applications to variational Bayes
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.
Efficient, Effective and Well Justified Estimation of Active Nodes within a Cluster
Reliable and efficient estimation of the size of a dynamically changing cluster in an IoT network is critical in its nominal operation.
Latent Factor Analysis of Gaussian Distributions under Graphical Constraints
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.
Coupling the reduced-order model and the generative model for an importance sampling estimator
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.
Algebraic Properties of Wyner Common Information Solution under Graphical Constraints
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
Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable.
