6 citations · 16 across the 7 of their papers we have counts for
7 papers
Doubly Robust Self-Training
Banghua Zhu, Mingyu Ding, Philip Jacobson +4
Self-training is an important technique for solving semi-supervised learning problems. It leverages unlabeled data by generating pseudo-labels and combining them with a limited lab…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Recommendation Systems with Distribution-Free Reliability Guarantees
Anastasios N. Angelopoulos, Karl Krauth, Stephen Bates +2
When building recommendation systems, we seek to output a helpful set of items to the user. Under the hood, a ranking model predicts which of two candidate items is better, and we…
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels
Yaodong Yu, Alexander Wei, Sai Praneeth Karimireddy +2
State-of-the-art federated learning methods can perform far worse than their centralized counterparts when clients have dissimilar data distributions. For neural networks, even whe…
Rank Diminishing in Deep Neural Networks
Ruili Feng, Kecheng Zheng, Yukun Huang +3
The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In pa…
Robust Calibration with Multi-domain Temperature Scaling
Yaodong Yu, Stephen Bates, Yi Ma +1
Uncertainty quantification is essential for the reliable deployment of machine learning models to high-stakes application domains. Uncertainty quantification is all the more challe…
Optimal Data Selection: An Online Distributed View
Mariel Werner, Anastasios Angelopoulos, Stephen Bates +1
The blessing of ubiquitous data also comes with a curse: the communication, storage, and labeling of massive, mostly redundant datasets. We seek to solve this problem at its core,…