Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.

Themotivation for this is that these processes will encourage exploration ofπ by simulating continuous velocity paths in between random event times at which the velocity changes.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.

Deep mixture-of-experts models have attracted a lot of attention for survival analysis problems, particularly for their ability to cluster similar patients together. In practice, grouping often comes at the expense of key metrics such as calibration error and predictive accuracy. This is due to the restrictive inductive bias that mixture-of-experts imposes, that predictions for individual patients must look like predictions for the group they're assigned to. Might we be able to discover patient group structure, where it exists, while improving calibration and predictive accuracy? In this work, we introduce several discrete-time deep mixture-of-experts (MoE)-based architectures for survival analysis problems, one of which achieves all desiderata: clustering, calibration, and predictive accuracy. We show that a key differentiator between this array of MoEs is how expressive their experts are. We find that more expressive experts that tailor predictions per patient outperform experts that rely on fixed group prototypes.

Survival analysis is central in many applications across medicine, engineering, economics and finance. It concerns time-to-event modeling: given a process that can generate an event of interest (e.g., death from disease, failure due to wear), the goal is to estimate the probability that an event occurs at any time t > 0 for an individual described by some input variables (or features, or covariates). Unlike standard regression settings, survival data are characterized by censoring, which means that for some instances, the exact event time is not observed (for example, when individuals remain event-free at the end of the study), and only the last recorded follow-up time is available. Traditional approaches to survival modeling rely on strong statistical assumptions linking input variables and risk. The Cox proportional hazards (CoxPH) model [1] remains the most widely used and best established method. The proportional hazards assumption implies that the instantaneous risk of event for two individuals differs by a constant factor over time. The CoxPH model is also linear, making it clear how each single input variable affects the outcome, but at the expense of missing interactions between features. In its original form, this relation is modeled through a linear regression on the features, though many extensions have been developed to relax linearity and improve performance in high-dimensional settings [2-4]. Despite its success, Cox regression is limited by the proportional hazards (PH) assumption, which is often unrealistic.

Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination without sacrificing calibration.