Student Name: George Vershinin Degree: Ph.D. Advisors: Prof. Omer Gurewitz & Prof. Asaf Cohen Title Talk: On Cost-Aware Designs for Sequential Hypothesis Testing

Abstract: We study the sequential hypothesis-testing framework, where a single decision maker adaptively selects sensing actions to identify the true hypothesis under an average-error constraint as swiftly as possible. By associating each action with a positive cost, the decision maker seeks to minimize the expected total cost, which embodies delay in the literal sense, in contrast to previous works that measure delay using the expected number of samples until the decision is made. We focus on two types of action costs: the simple constant cost model and the random cost model. Under the random cost model, we study two cost-revelation models: ex-post, in which the cost is revealed only after the sample is obtained (embodying billing), and ex-ante, in which the cost accrues before the sample is acquired (embodying latency). Additionally, the decision maker may opt to abandon the current action and apply a different action (i.e., preemption). We prove that the optimal (cost-aware) strategies in such settings should maximize the ratio of the information bits gained per sample to the expected cost per sample induced by the action-selecting policy rather than optimizing the intuitive per-step information gain per cost. In addition, the effects of preemption under two cost-revelation models are analyzed; In the ex-post model, preemption does not affect performance, and the problem reduces to the constant-cost setting. In contrast, in the ex-ante model, preemption inflates the number of actions taken but may boost performance. We characterize when preemption is beneficial and study several families in detail. Building on numerous insights, we adapt classical sequential testing schemes to cost-aware settings while preserving their asymptotic guarantees and improving performance in the finite regime.