Recent years have seen a resurgence of interest in analysis of non-worst-case instance models, in order to gain new algorithmic insights and bring theory and practice together on problems where hardness results preclude strong worst-case bounds. These models can involve deterministic stability conditions, probabilistic assumptions, mixed probabilistic/adversarial models, or novel types of performance guarantees. This workshop will bring together researchers to discuss new results, insights, and challenges in analysis of algorithms beyond the worst case.