21 citations · 50 across the 17 of their papers we have counts for
5 papers · 1 filter
Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
George Panagopoulos
Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice. However, the two communities…
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Roman Bresson, Giannis Nikolentzos, George Panagopoulos +3
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood a…
Uplift Modeling Under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros +1
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such…
Maximizing Influence with Graph Neural Networks
George Panagopoulos, Nikolaos Tziortziotis, Michalis Vazirgiannis +1
Finding the seed set that maximizes the influence spread over a network is a well-known NP-hard problem. Though a greedy algorithm can provide near-optimal solutions, the subproble…
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
Benedek Rozemberczki, Paul Scherer, Yixuan He +8
We present PyTorch Geometric Temporal a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of…