We consider the problem of fitting variational posterior approximations using stochastic optimization methods.

Partial-label learning(PLL) is a multi-class classification problem, where each training example isassociated withasetofcandidate labels.

In contrast to regularizationbased approaches, we formulate the adversarially robust learning problem as one of loss minimization with a Lipschitz constraint, and show that the saddle point of the associated Lagrangian is characterized by a Poisson equation with weighted Laplace operator. Further, the weighting for the Laplace operator is given by the Lagrange multiplier for the Lipschitz constraint, which modulates the sensitivity of the minimizer to perturbations.

Deep reinforcement learning has shown great success in automatic policy learning for various sequential decision-making problems, such as training AI agents to defeat professional players in sophisticated games [74, 65, 24, 37] and controlling robots to accomplish complicated tasks [33, 38].

Multi-View Representation Learning (MVRL) aims to learn a shared representation of multiple observations from different types of views.

Amidst therecent strides inevaluating LargeLanguage Models forCode (Code LLMs), existing benchmarks havemainly focused onthefunctional correctness of generated code, neglecting the importance of their computational efficiency.