85 citations · 196 across the 12 of their papers we have counts for
16 papers
Hierarchical Domain Generalization
Chenxiao Yang, Zhiyuan Li, Shai Ben-David +1
We study hierarchical domain generalization as a problem of extrapolation from finite observed regions to an entire instance space, replacing i.i.d. sampling with arbitrary domain…
Private Distribution Learning with Public Data: The View from Sample Compression
Shai Ben-David, Alex Bie, Clément L. Canonne +2
We study the problem of private distribution learning with access to public data. In this setup, which we refer to as public-private learning, the learner is given public and priva…
Impossibility results for fair representations
Tosca Lechner, Shai Ben-David, Sushant Agarwal +1
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious intere…
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet +3
In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes a…
Semi-supervised clustering for de-duplication
Shrinu Kushagra, Shai Ben-David, Ihab Ilyas
Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering prob…
Clustering - What Both Theoreticians and Practitioners are Doing Wrong
Shai Ben-David
Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being pu…