Classical bilevel optimization is referred to as the case where there is no consensus constraint but with only two levels of the minimization subproblems, i.e.,

A recent line of research investigates how strategic agents may respond to such scoring tools to receive favorable assessments. While prior work has focused on the short-term strategic interactions between a decision-making institution (modeled as a principal) and individual decision-subjects (modeled as agents), we investigate interactions spanning multiple time-steps . In particular, we consider settings in which the agent's effort investment

We use three cameras and three screens to capture our dataset; please see Table 1 for the specifications. The following algorithms (Procedure 1 and Procedure 2) show the joint optimization method and the baseline alternating optimization method compared in the ablation study in Section 5.1 of the main paper. Procedure 1 The joint optimization algorithm.Input: Focused image M with moiré patterns and defocused blur image B without moiré patterns; Output: Estimated moiré-free image C; Procedure 2 The alternating optimization algorithm.Input: Focused image M with moiré patterns and defocused blur image B without moiré patterns; Output: Estimated moiré-free image C; We also test our model on a smartphone HUA WEI P30 PRO. To test on the real world examples, we do some preprocessing, e.g., alignment. Figure 1: Examples captured from natural scenes.

"one-shot" formulation of the Knowledge Gradient, enabled by a combination of

Efficient approximation and sampling methods of intractable distributions plays a key role in probabilistic machine learning, especially Bayesian inference.