19 citations · 53 across the 13 of their papers we have counts for
14 papers
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
Yuyang Deng, Fuli Qiao, Mehrdad Mahdavi
Stochastic compositional minimax problems are prevalent in machine learning, yet there are only limited established on the convergence of this class of problems. In this paper, we…
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…
On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method
Weilin Cong, Jian Kang, Hanghang Tong +1
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves ov…
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…
Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi +1
We present quantum algorithms for sampling from non-logconcave probability distributions in the form of . Here, can be written as a finite sum $f(x):…
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…