39 citations · 65 across the 4 of their papers we have counts for
5 papers · 1 filter
Scalable Neural Data Server: A Data Recommender for Transfer Learning
Tianshi Cao, Sasha Doubov, David Acuna +1
Absence of large-scale labeled data in the practitioner's target domain can be a bottleneck to applying machine learning algorithms in practice. Transfer learning is a popular stra…
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Tianshi Cao, Alex Bie, Arash Vahdat +2
Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricte…
A Benchmark of Medical Out of Distribution Detection
Tianshi Cao, Chin-Wei Huang, David Yu-Tung Hui +1
Motivation: Deep learning models deployed for use on medical tasks can be equipped with Out-of-Distribution Detection (OoDD) methods in order to avoid erroneous predictions. Howeve…
Zero-Shot Compositional Policy Learning via Language Grounding
Tianshi Cao, Jingkang Wang, Yining Zhang +1
Despite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans…
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
Tianshi Cao, Marc Law, Sanja Fidler
Few-shot classification is the task of predicting the category of an example from a set of few labeled examples. The number of labeled examples per category is called the number of…