70 citations · 170 across the 13 of their papers we have counts for
13 papers
Bag of Policies for Distributional Deep Exploration
Asen Nachkov, Luchen Li, Giulia Luise +2
Efficient exploration in complex environments remains a major challenge for reinforcement learning (RL). Compared to previous Thompson sampling-inspired mechanisms that enable temp…
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero +3
Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges. This inductive bias leads to a phenomenon kn…
Schedule-Robust Online Continual Learning
Ruohan Wang, Marco Ciccone, Giulia Luise +3
A continual learning (CL) algorithm learns from a non-stationary data stream. The non-stationarity is modeled by some schedule that determines how data is presented over time. Most…
Meta Optimal Transport
Brandon Amos, Samuel Cohen, Giulia Luise +1
We study the use of amortized optimization to predict optimal transport (OT) maps from the input measures, which we call Meta OT. This helps repeatedly solve similar OT problems be…
Heterogeneous manifolds for curvature-aware graph embedding
Francesco Di Giovanni, Giulia Luise, Michael Bronstein
Graph embeddings, wherein the nodes of the graph are represented by points in a continuous space, are used in a broad range of Graph ML applications. The quality of such embeddings…
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Paul Festor, Giulia Luise, Matthieu Komorowski +1
Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, aut…