Juan Cerviño

IEEE Trans. Signal Process., 2023

FastSample: Accelerating Distributed Graph Neural Network Training for Billion-Scale Graphs.

[BibT_eX]

[DOI]

CoRR, 2023

Intrinsically motivated graph exploration using network theories of human curiosity.

[BibT_eX]

[DOI]

Shubhankar P. Patankar

CoRR, 2023

Learning Globally Smooth Functions on Manifolds.

[BibT_eX]

[DOI]

Luiz F. O. Chamon

Benjamin David Haeffele

René Vidal

Proceedings of the International Conference on Machine Learning, 2023

Training Graph Neural Networks on Growing Stochastic Graphs.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2023

Multi-Task Bias-Variance Trade-Off Through Functional Constraints.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Federated Representation Learning via Maximal Coding Rate Reduction.

[BibT_eX]

[DOI]

Navid Naderializadeh

CoRR, 2022

An Agnostic Approach to Federated Learning with Class Imbalance.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Training Stable Graph Neural Networks Through Constrained Learning.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2021

Increase and Conquer: Training Graph Neural Networks on Growing Graphs.

[BibT_eX]

[DOI]

CoRR, 2021

Multi-task Supervised Learning via Cross-learning.

[BibT_eX]

[DOI]

Proceedings of the 29th European Signal Processing Conference, 2021

2020

Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples.

[BibT_eX]

[DOI]

Harshat Kumar