Conditional quantiles obtained via regression are used as a robust alternative to classical conditional means in econometrics and statistics, as they can capture the uncertainty in a prediction, and model tail behaviors, while making very few distributional assumptions. In this work, we extend the notion of conditional quantiles to the binary classification setting—allowing us to quantify the uncertainty in the predictions, increase resilience to label noise, and provide new insights into the functions learnt by the models. We accomplish this by defining a new loss called binary quantile regression loss. We compute the Lipschitz constant of the proposed loss and show that its curvature is bounded under some regularity conditions. These properties are later used to characterize the error rates of the learning algorithms and to accelerate the training regime with using Lipschitz adaptive learning rates. We leverage the estimated quantiles to obtain individualized confidence scores that provide an accurate measure of a prediction being misclassified. We aggregate these scores to provide two additional metrics, namely, confidence score and retention rate, which can be used to withhold decisions and increase model accuracy. We also study the robustness of the proposed nonparametric binary quantile classification framework, and finally, we demonstrate that quantiles aid in explainability as they can be used to obtain several univariate summary statistics that can be directly applied to existing explanation tools.

Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems.

Harsh working environments and work-related stress have been known to contribute to mental health problems such as anxiety, depression, and suicidal ideation. As such, it is paramount to create solutions that can both detect employee unhappiness and find the root cause of the problem. While prior works have examined causes of mental health using machine learning, they typically focus on general mental health analysis, with few of them focusing on explainable solutions or looking at the workplace-specific setting. r/antiwork is a subreddit for the antiwork movement, which is the desire to stop working altogether. Using this subreddit as a proxy for work environment dissatisfaction, we create a new dataset for antiwork sentiment detection and subsequently train a model that highlights the words with antiwork sentiments. Following this, we performed a qualitative and quantitative analysis to uncover some of the key insights into the mindset of individuals who identify with the antiwork movement and how their working environments influenced them. We find that working environments that do not give employees authority or responsibility, frustrating recruiting experiences, and unfair compensation, are some of the leading causes of the antiwork sentiment, resulting in a lack of self-confidence and motivation among their employees.