Our method efficiently infers the trial-to-trial changes inananimal'spolicy,and decomposes those changes into a learning component and a noise component.

We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions.

One leading algorithmic paradigm on NISQ computers is theVariational Quantum Algorithm (VQA) with a few prominent examples like the Variational Quantum Eignensolver (VQE) [50], quantum approximate optimization algorithm (QAOA) [20], and more in [4]. Quantum machine learning isafast-developing emerging field (e.g., see the survey [5]) where variational quantum algorithms (VQAs) (e.g., see thesurvey[4]areoneofthemost promising candidates forNISQ applications.

However, the performance of label-mapping-based visual prompting (LM-VP) under adversarial attacks and MIAs lacks evaluation.