Memory Regret

Memory-regret trade-offs in online learning explore the fundamental tension between minimizing the cumulative error (regret) of an algorithm and limiting its memory usage, particularly when dealing with massive datasets or streams of data. Current research focuses on establishing tight theoretical bounds on this trade-off within various models, including streaming bandits and online learning with experts, often employing novel algorithmic approaches and information-theoretic techniques. These studies are crucial for developing efficient algorithms for large-scale applications where memory is a significant constraint, impacting fields such as online advertising, recommendation systems, and clinical trials. The ultimate goal is to design algorithms that achieve near-optimal performance with minimal memory requirements.