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f-Domain-Adversarial Learning: Theory and Algorithms
Unsupervised domain adaptation is used in many machine learning applicat...
06/21/2021 ∙ by David Acuna, et al. ∙
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Quantifying and Improving Transferability in Domain Generalization
Out-of-distribution generalization is one of the key challenges when tra...
06/07/2021 ∙ by Guojun Zhang, et al. ∙
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Newton-type Methods for Minimax Optimization
Differential games, in particular two-player sequential games (a.k.a. mi...
06/25/2020 ∙ by Guojun Zhang, et al. ∙
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FedMGDA+: Federated Learning meets Multi-objective Optimization
Federated learning has emerged as a promising, massively distributed way...
06/20/2020 ∙ by Zeou Hu, et al. ∙
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Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization
Convergence to a saddle point for convex-concave functions has been stud...
02/27/2020 ∙ by Guojun Zhang, et al. ∙
9
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Convergence Behaviour of Some Gradient-Based Methods on Bilinear Zero-Sum Games
Min-max formulations have attracted great attention in the ML community ...
08/15/2019 ∙ by Guojun Zhang, et al. ∙
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Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games
Min-max optimization has attracted much attention in the machine learnin...
08/15/2019 ∙ by Guojun Zhang, et al. ∙
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Comparing EM with GD in Mixture Models of Two Components
The expectation-maximization (EM) algorithm has been widely used in mini...
07/08/2019 ∙ by Guojun Zhang, et al. ∙
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