We study the problem of fair binary classification using the notion of Equal Opportunity.

Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment remain understudied. These moments are infrequent in naturalistic data, and thus training a supervised learning (SL) model requires costly manual data annotation. We applied semi-supervised learning (SSL) to leverage targeted labeled and unlabeled clips for training multimodal (audio, facial, text) deep features to predict non-fluid or unenjoyable moments in holdout videoconference sessions. The modality-fused co-training SSL achieved an ROC-AUC of 0.9 and an F1 score of 0.6, outperforming SL models by up to 4% with the same amount of labeled data. Remarkably, the best SSL model with just 8% labeled data matched 96% of the SL model's full-data performance. This shows an annotation-efficient framework for modeling videoconference experience.

We further explore the training stability of FixMatch when using pre-trained models on various tasks. Figure 2 illustrates several failure cases of FixMatch.

Despite its popularity, self-training is well-believed to be unreliable and often leads to training instability.

Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled. This work proposes an unsupervised learning framework for CO problems.

Moreover, we will conduct additional experiments to further evaluate our method in Section C. Furthermore, we provide the standard deviation results that correspond to the main paper in Section D. Finally, we will discuss the limitations and social impact of our method in Section E. VisDA2017 datasets, we set the batch size to 64. Other implementation details are presented below. It contains 3 domains: "Amazon" (A), "DSLR" (D), and "Webcam" (W), and each domain is composed of 31 classes. Shared learning rate decay factor 0.2 # training iteration in which learning rate decay starts 400,000 # training iteration in which consistency coefficient ramp up starts 200,000 Supervised Initial learning rate 0.003 Π-Model [6, 10] Initial learning rate 3 10 CAFA framework, which includes class-sharing data detection and feature adaptation . Here we use PI as the backbone method.

The OV A-classifier outputs the confidence score of a sample being an inlier, providing a threshold to detect outliers.

While a lot of recent efforts have been made on generalizing pose estimation to novel object instances within the same category, namely category-level 6D pose estimation, it is still restricted in constrained environments given the limited number of annotated data.

But not all unlabeled data are equal. In this paper we study how to use a different weight for every unlabeled example. Manual tuning of all those weights - as done in prior work - is no longer possible.

We compare our method with state-of-the-art SSL and BSL methods through extensive experiments on standard SSL benchmarks.