170 citations · 231 across the 14 of their papers we have counts for
20 papers · 1 filter
On the Generalization Ability of Unsupervised Pretraining
Yuyang Deng, Junyuan Hong, Jiayu Zhou +1
Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of…
Distributed Personalized Empirical Risk Minimization
Yuyang Deng, Mohammad Mahdi Kamani, Pouria Mahdavinia +1
This paper advocates a new paradigm Personalized Empirical Risk Minimization (PERM) to facilitate learning from heterogeneous data sources without imposing stringent constraints on…
Understanding Deep Gradient Leakage via Inversion Influence Functions
Haobo Zhang, Junyuan Hong, Yuyang Deng +2
Deep Gradient Leakage (DGL) is a highly effective attack that recovers private training images from gradient vectors. This attack casts significant privacy challenges on distribute…
Mixture Weight Estimation and Model Prediction in Multi-source Multi-target Domain Adaptation
Yuyang Deng, Ilja Kuzborskij, Mehrdad Mahdavi
We consider the problem of learning a model from multiple heterogeneous sources with the goal of performing well on a new target distribution. The goal of learner is to mix these d…
Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems
Pouria Mahdavinia, Yuyang Deng, Haochuan Li +1
Despite the established convergence theory of Optimistic Gradient Descent Ascent (OGDA) and Extragradient (EG) methods for the convex-concave minimax problems, little is known abou…
Learning Distributionally Robust Models at Scale via Composite Optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi +1
To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existin…