Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user.

Recently, a new document metric called the word mover's distance (WMD) has been proposed with unprecedented results on kNN-based document classification. The WMD elevates high-quality word embeddings to a document metric by formulating the distance between two documents as an optimal transport problem between the embedded words. However, the document distances are entirely unsupervised and lack a mechanism to incorporate supervision when available. In this paper we propose an efficient technique to learn a supervised metric, which we call the Supervised-WMD (S-WMD) metric.

Formally, the CCA problem is defined as follows.

Since proxy invariance indiff approaches data.

This paper provides a unified view to explain different adversarial attacks and defensemethods,i.e.

Fairness and robustness are critical elements of Trustworthy AI that need to be addressedtogether.

Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning.

Thank you for your detailed reviews and comments. Wehope our clarifications, which we will include in the final1 version of the paper, will strengthen your confidence in the novelty and significance of our results. Both speed up DPP sampling given a polynomial time pre-processing step.

Graph embedding has been intensively studied recently, due to the advance of various neural networkmodels.

It works both in the stochastic and the deterministic settings, without hurting the algorithm'sperformance. As applications, our reduction turns Natasha2 into a first-order method without hurting its theoretical performance. It also converts SGD, GD, SCSG, and SVRG into algorithms finding approximate local minima, outperforming some bestknownresults.