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08/22/2023
Understanding Hessian Alignment for Domain Generalization
Out-of-distribution (OOD) generalization is a critical ability for deep ...
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08/18/2023
Normalization Is All You Need: Understanding Layer-Normalized Federated Learning under Extreme Label Shift
Layer normalization (LN) is a widely adopted deep learning technique esp...
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03/24/2023
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry sin...
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02/06/2023
Private GANs, Revisited
We show that the canonical approach for training differentially private ...
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11/27/2022
Label Alignment Regularization for Distribution Shift
Recent work reported the label alignment property in a supervised learni...
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08/05/2022
DP^2-VAE: Differentially Private Pre-trained Variational Autoencoders
Modern machine learning systems achieve great success when trained on la...
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06/20/2022
Mitigating Data Heterogeneity in Federated Learning with Data Augmentation
Federated Learning (FL) is a prominent framework that enables training a...
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06/20/2022
Robust One Round Federated Learning with Predictive Space Bayesian Inference
Making predictions robust is an important challenge. A separate challeng...
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06/13/2022
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
In typical scenarios where the Federated Learning (FL) framework applies...
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02/10/2022
Domain Adversarial Training: A Game Perspective
The dominant line of work in domain adaptation has focused on learning i...
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02/03/2022
Equality Is Not Equity: Proportional Fairness in Federated Learning
Ensuring fairness of machine learning (ML) algorithms is becoming an inc...
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06/21/2021
f-Domain-Adversarial Learning: Theory and Algorithms
Unsupervised domain adaptation is used in many machine learning applicat...
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06/07/2021
Quantifying and Improving Transferability in Domain Generalization
Out-of-distribution generalization is one of the key challenges when tra...
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06/25/2020
Newton-type Methods for Minimax Optimization
Differential games, in particular two-player sequential games (a.k.a. mi...
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06/20/2020
FedMGDA+: Federated Learning meets Multi-objective Optimization
Federated learning has emerged as a promising, massively distributed way...
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02/27/2020
Optimality and Stability in Non-Convex-Non-Concave Min-Max Optimization
Convergence to a saddle point for convex-concave functions has been stud...
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08/15/2019
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Zero-Sum Games
Min-max formulations have attracted great attention in the ML community ...
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08/15/2019
Convergence Behaviour of Some Gradient-Based Methods on Bilinear Games
Min-max optimization has attracted much attention in the machine learnin...
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07/08/2019
