53 citations · 94 across the 9 of their papers we have counts for
10 papers
Generalization and Representational Limits of Graph Neural Networks
Vikas K. Garg, Stefanie Jegelka, Tommi Jaakkola
We address two fundamental questions about graph neural networks (GNNs). First, we prove that several important graph properties cannot be computed by GNNs that rely entirely on lo…
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Vikas K. Garg, Adam Kalai, Katrina Ligett +1
Domain generalization is the problem of machine learning when the training data and the test data come from different data domains. We present a simple theoretical model of learnin…
Multiresolution Transformer Networks: Recurrence is Not Essential for Modeling Hierarchical Structure
Vikas K. Garg, Inderjit S. Dhillon, Hsiang-Fu Yu
The architecture of Transformer is based entirely on self-attention, and has been shown to outperform models that employ recurrence on sequence transduction tasks such as machine t…
Strategic Prediction with Latent Aggregative Games
Vikas K. Garg, Tommi Jaakkola
We introduce a new class of context dependent, incomplete information games to serve as structured prediction models for settings with significant strategic interactions. Our games…
Solving graph compression via optimal transport
Vikas K. Garg, Tommi Jaakkola
We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges i…
Learning SMaLL Predictors
Vikas K. Garg, Ofer Dekel, Lin Xiao
We present a new machine learning technique for training small resource-constrained predictors. Our algorithm, the Sparse Multiprototype Linear Learner (SMaLL), is inspired by the…