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07/11/2023
Intrinsically motivated graph exploration using network theories of human curiosity
Intrinsically motivated exploration has proven useful for reinforcement ...
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10/27/2022
Multi-task Bias-Variance Trade-off Through Functional Constraints
Multi-task learning aims to acquire a set of functions, either regressor...
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10/27/2022
Training Graph Neural Networks on Growing Stochastic Graphs
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meani...
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10/01/2022
Learning Globally Smooth Functions on Manifolds
Smoothness and low dimensional structures play central roles in improvin...
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10/01/2022
Federated Representation Learning via Maximal Coding Rate Reduction
We propose a federated methodology to learn low-dimensional representati...
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10/07/2021
Training Stable Graph Neural Networks Through Constrained Learning
Graph Neural Networks (GNN) rely on graph convolutions to learn features...
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06/07/2021
Increase and Conquer: Training Graph Neural Networks on Growing Graphs
Graph neural networks (GNNs) use graph convolutions to exploit network i...
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10/24/2020