115 citations · 138 across the 7 of their papers we have counts for
4 papers · 1 filter
Domain Adversarial Training: A Game Perspective
David Acuna, Marc T Law, Guojun Zhang +1
The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a…
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
Or Litany, Haggai Maron, David Acuna +3
Standard Federated Learning (FL) techniques are limited to clients with identical network architectures. This restricts potential use-cases like cross-platform training or inter-or…
f-Domain-Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law +1
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labele…
Complex Momentum for Optimization in Games
Jonathan Lorraine, David Acuna, Paul Vicol +1
We generalize gradient descent with momentum for optimization in differentiable games to have complex-valued momentum. We give theoretical motivation for our method by proving conv…