Your monitor needs a monitor arm, and I've been testing every single one I can get my hands on to see which is best. A monitor arm should be one of those simple products you buy once and never think about again. But I've seen horror stories of cheap, knock-off models that collapse, damaging both the desk and the monitor. Anything that mounts a very heavy piece of expensive tech like a high-end monitor should be high-quality, which is true of all the options below. Each of the monitor arms on our list have been hand-tested by us. Most are currently clamped down to a desk of one of our product reviewers.

Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of fairness align with legal requirements, yet many methods assume access to detailed knowledge of the underlying causal graph, which is a demanding assumption in practice. We propose a learning framework that achieves interventional fairness by leveraging a causal graph over \textit{clusters of variables}, which is substantially easier to estimate than a variable-level graph. With possible \textit{adjustment cluster sets} identified from such a cluster causal graph, our framework trains a prediction model by reducing the worst-case discrepancy between interventional distributions across these sets. To this end, we develop a computationally efficient barycenter kernel maximum mean discrepancy (MMD) that scales favorably with the number of sensitive attribute values. Extensive experiments show that our framework strikes a better balance between fairness and accuracy than existing approaches, highlighting its effectiveness under limited causal graph knowledge.

Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional learning problems to increase the confidence of a given predictor.

It represents a radiance field using tri-planes with three multi-resolutions for each plane: 128, 256, and 512 in both height and width, and 32 in feature depth. However, any MDE model can be utilized within our framework [19, 13, 12]. The training process takes approximately 3 hours. In other words, we can rewrite the above scheme as a closed problem. The results of DDP-NeRF with in-domain priors are 20.96,

However, the exact formula for front-door adjustment depends on the structure of the graph, which is difficult to learn in practice.

CaST, which consistently outperforms existing methods with good interpretability.