29 citations · 195 across the 32 of their papers we have counts for
4 papers · 1 filter
Adversarial Training Helps Transfer Learning via Better Representations
Zhun Deng, Linjun Zhang, Kailas Vodrahalli +2
Transfer learning aims to leverage models pre-trained on source data to efficiently adapt to target setting, where only limited data are available for model fine-tuning. Recent wor…
Meta-Learning with Fewer Tasks through Task Interpolation
Huaxiu Yao, Linjun Zhang, Chelsea Finn
Meta-learning enables algorithms to quickly learn a newly encountered task with just a few labeled examples by transferring previously learned knowledge. However, the bottleneck of…
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…
When and How Mixup Improves Calibration
Linjun Zhang, Zhun Deng, Kenji Kawaguchi +1
In many machine learning applications, it is important for the model to provide confidence scores that accurately capture its prediction uncertainty. Although modern learning metho…