6 papers
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Edgeworth Accountant: An Analytical Approach to Differential Privacy Composition
Hua Wang, Sheng Gao, Huanyu Zhang +3
In privacy-preserving data analysis, many procedures and algorithms are structured as compositions of multiple private building blocks. As such, an important question is how to eff…
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang, Hongyang R. Zhang, Sen Wu +2
The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following question: when does combining t…
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
Shuxiao Chen, Qinqing Zheng, Qi Long +1
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often originate from distinct yet not entirely unrelate…
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an…
Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review
Yuling Yan, Weijie J. Su, Jianqing Fan
In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we…