  Machine Learning has made remarkable progress in a wide range of fields. In
many scenarios, learning is performed on datasets involving sensitive
information, in which privacy protection is essential for learning algorithms.
In this work, we study pure private learning in the agnostic model -- a
framework reflecting the learning process in practice. We examine the number of
users required under item-level (where each user contributes one example) and
user-level (where each user contributes multiple examples) privacy and derive
several improved upper bounds. For item-level privacy, our algorithm achieves a
near optimal bound for general concept classes. We extend this to the
user-level setting, rendering a tighter upper bound than the one proved by
Ghazi et al. (2023). Lastly, we consider the problem of learning thresholds
under user-level privacy and present an algorithm with a nearly tight user
complexity.
