1 citations · 2 across the 5 of their papers we have counts for
5 papers
Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models
Mariana Vargas Vieyra
Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs. Widely used in several domains, including healthcare and churn pred…
Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK
Ivona Krchova, Mariana Vargas Vieyra, Mario Scriminaci +1
Machine learning development critically depends on access to high-quality data. However, increasing restrictions due to privacy, proprietary interests, and ethical concerns have cr…
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
Paul Tiwald, Ivona Krchova, Andrey Sidorenko +3
Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regr…
Deep End-to-End Survival Analysis with Temporal Consistency
Mariana Vargas Vieyra, Pascal Frossard
In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspiration from Reinforcement Learni…
Learning Generative Models with Goal-conditioned Reinforcement Learning
Mariana Vargas Vieyra, Pierre Ménard
We present a novel, alternative framework for learning generative models with goal-conditioned reinforcement learning. We define two agents, a goal conditioned agent (GC-agent) and…