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Found 19 papers, 3 papers with code
Six Levels of Privacy: A Framework for Financial Synthetic Data
In addition to the benefits it provides, such as improved financial modeling and better testing procedures, it poses privacy risks as well.
Downstream Task-Oriented Generative Model Selections on Synthetic Data Training for Fraud Detection Models
Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance.
Synthetic Data Applications in Finance
Synthetic data has made tremendous strides in various commercial settings including finance, healthcare, and virtual reality.
Fair Coresets via Optimal Transport
In this work, we present fair Wasserstein coresets (FWC), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Focusing on per-instance differential privacy (pDP), our framework elucidates the potential privacy leakage for each data point in a given training dataset, offering insights into data preprocessing to reduce privacy risks of the synthetic dataset generation via DDMs.
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?
However, these models face challenges in generating large attributed graphs due to the complex attribute-structure correlations and the large size of these graphs.
A supervised generative optimization approach for tabular data
Synthetic data generation has emerged as a crucial topic for financial institutions, driven by multiple factors, such as privacy protection and data augmentation.
Differentially Private Synthetic Data Using KD-Trees
Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge.
Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe
Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data.
Online Learning for Mixture of Multivariate Hawkes Processes
Online learning of Hawkes processes has received increasing attention in the last couple of years especially for modeling a network of actors.
Differentially Private Learning of Hawkes Processes
Hawkes processes have recently gained increasing attention from the machine learning community for their versatility in modeling event sequence data.
Bandit Sampling for Multiplex Networks
Most of the existing work focuses primarily on the monoplex setting where we have access to a network with only a single type of connection between entities.
Graph Belief Propagation Networks
With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem.
Goal recognition via model-based and model-free techniques
In this paper, we adapt state-of-the-art learning techniques to goal recognition, and compare model-based and model-free approaches in different domains.
Heuristics for Link Prediction in Multiplex Networks
Through experiments with simulated and real world scientific collaboration, transportation and global trade networks, we demonstrate that the proposed heuristics show increased performance with the richness of connection type correlation structure and significantly outperform their baseline heuristics for ordinary networks with a single connection type.
Explicit Group Sparse Projection with Applications to Deep Learning and NMF
Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.
Conservative Exploration using Interleaving
In many practical problems, a learning agent may want to learn the best action in hindsight without ever taking a bad action, which is significantly worse than the default production action.
Block Coordinate Descent for Sparse NMF
However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms.
