Deep Learning
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss An Zhang
An Zhang and Leheng Sheng contribute equally to this work. Zhibo Cai is the corresponding author.
ConformalTime-SeriesForecasting
Time-series forecasting tasks are central to a broad range of application domains, including stock pricepredictions [1,2],servicedemandforecasting [3,4],andmedicalprognoses[5-7].
Appendix to " Adam with Bandit Sampling for Deep Learning "
According to Theorem 4. 1 in [1], the convergence rate of Adam is We prove Lemma 1 using the framework of online learning with bandit feedback. Let's consider a special case where It follows simply by plugging Lemma 3 into Theorem 2. In the main paper, we compared our method with Adam and Adam with importance sampling. In the main paper, we have shown the plots of loss value vs. wall clock time. Here, we include some plots of error rate vs. wall