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Found 6 papers, 2 papers with code
Synthetic Tabular Data Validation: A Divergence-Based Approach
Our core contribution lies in applying a divergence estimator to build a validation metric considering the joint distribution of real and synthetic data.
An improved tabular data generator with VAE-GMM integration
Furthermore, our model offers enhanced flexibility by allowing the use of various differentiable distributions for individual features, making it possible to handle both continuous and discrete data types.
SAVAE: Leveraging the variational Bayes autoencoder for survival analysis
As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored medical data.
Learning Parametric Closed-Loop Policies for Markov Potential Games
This is a considerable improvement over the previously standard approach for the CL analysis of MPGs, which gives no approximate solution if no NE belongs to the chosen parametric family, and which is practical only for simple parametric forms.
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL).
Distributed Policy Evaluation Under Multiple Behavior Strategies
We apply diffusion strategies to develop a fully-distributed cooperative reinforcement learning algorithm in which agents in a network communicate only with their immediate neighbors to improve predictions about their environment.
