Aggregated Feedback

Aggregated feedback in machine learning focuses on improving model training efficiency and performance by utilizing summarized or batched reward signals, rather than individual data points. Current research explores algorithms like reinforcement learning from statistical feedback (RLSF) and Gaussian Process Optimisation (GPOO), adapting bandit algorithms to handle this type of data, often in high-dimensional settings. This approach is particularly relevant for applications where obtaining precise individual feedback is costly or impractical, offering significant potential for improving the scalability and cost-effectiveness of various machine learning systems. The development of provably efficient algorithms for handling aggregated feedback is a key area of ongoing investigation.