518 citations · 1.3k across the 51 of their papers we have counts for
9 papers · 1 filter
Envisioning Future Deep Learning Theories: Some Basic Concepts and Characteristics
Weijie J. Su
To advance deep learning methodologies in the next decade, a theoretical framework for reasoning about modern neural networks is needed. While efforts are increasing toward demysti…
Oneshot Differentially Private Top-k Selection
Gang Qiao, Weijie J. Su, Li Zhang
Being able to efficiently and accurately select the top- elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we pr…
Weighted Training for Cross-Task Learning
Shuxiao Chen, Koby Crammer, Hangfeng He +2
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance be…
Characterizing the SLOPE Trade-off: A Variational Perspective and the Donoho-Tanner Limit
Zhiqi Bu, Jason Klusowski, Cynthia Rush +1
Sorted l1 regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. I…
Rejoinder: Gaussian Differential Privacy
Jinshuo Dong, Aaron Roth, Weijie J. Su
In this rejoinder, we aim to address two broad issues that cover most comments made in the discussion. First, we discuss some theoretical aspects of our work and comment on how thi…
A Central Limit Theorem for Differentially Private Query Answering
Jinshuo Dong, Weijie J. Su, Linjun Zhang
Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The ce…
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…
Federated -Differential Privacy
Qinqing Zheng, Shuxiao Chen, Qi Long +1
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their lo…
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training
Cong Fang, Hangfeng He, Qi Long +1
In this paper, we introduce the \textit{Layer-Peeled Model}, a nonconvex yet analytically tractable optimization program, in a quest to better understand deep neural networks that…