Formally, the CCA problem is defined as follows.

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.

The exploration bonus is an effective approach to manage the explorationexploitation trade-offinMarkovDecision Processes (MDPs).

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.

Discovering clusters in bipartite graphs has been researched in many different settings. However, most of these algorithms were heuristics and do not provide theoretical guarantees for the quality oftheir results.