Typically, preference optimization is approached as an offline supervised learning task using manually crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under-explored.

These limitations hinder GNNs' ability to capture long-range dependencies in graphs, ultimately

In this paper, we review the recent development of classical federated primal dual methods and point out a serious common defect of such methods in non-convex scenarios, which we say is a "dual drift" caused by dual

Topic models have been evolving rapidly over the years, from conventional to recent neural models.

Among all the contributing factors, the quality and selection of data is becoming increasingly recognized for its importance in training LLMs effectively.