970 citations · 3k across the 88 of their papers we have counts for
9 papers · 1 filter
Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
Tasuku Soma, Khashayar Gatmiry, Sharut Gupta +1
Distributionally robust optimization (DRO) can improve the robustness and fairness of learning methods. In this paper, we devise stochastic algorithms for a class of DRO problems i…
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Ching-Yao Chuang, Stefanie Jegelka
Understanding generalization and robustness of machine learning models fundamentally relies on assuming an appropriate metric on the data space. Identifying such a metric is partic…
InfoOT: Information Maximizing Optimal Transport
Ching-Yao Chuang, Stefanie Jegelka, David Alvarez-Melis
Optimal transport aligns samples across distributions by minimizing the transportation cost between them, e.g., the geometric distances. Yet, it ignores coherence structure in the…
On the generalization of learning algorithms that do not converge
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry +1
Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural ne…
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Nikolaos Karalias, Joshua Robinson, Andreas Loukas +1
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (1) not naturally a…
Theory of Graph Neural Networks: Representation and Learning
Stefanie Jegelka
Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular learning model for prediction tasks on nodes, graph…
Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Derek Lim, Joshua Robinson, Lingxiao Zhao +4
We introduce SignNet and BasisNet -- new neural architectures that are invariant to two key symmetries displayed by eigenvectors: (i) sign flips, since if is an eigenvector the…
Robust Contrastive Learning against Noisy Views
Ching-Yao Chuang, R Devon Hjelm, Xin Wang +5
Contrastive learning relies on an assumption that positive pairs contain related views, e.g., patches of an image or co-occurring multimodal signals of a video, that share certain…
Training invariances and the low-rank phenomenon: beyond linear networks
Thien Le, Stefanie Jegelka
The implicit bias induced by the training of neural networks has become a topic of rigorous study. In the limit of gradient flow and gradient descent with appropriate step size, it…