Instance Dependent

Instance-dependent analysis in machine learning focuses on developing algorithms and theoretical bounds that reflect the specific characteristics of individual problems, rather than relying on worst-case scenarios. Current research emphasizes deriving instance-dependent regret and sample complexity bounds for various models, including bandits, reinforcement learning (with both tabular and function approximation settings), and stochastic optimization, often employing techniques like variance reduction and pessimism. This shift towards instance-specific analysis provides more accurate performance predictions and enables the design of more efficient algorithms tailored to the nuances of particular datasets and problem structures, ultimately leading to improved practical performance in diverse applications.