Model Selection Aggregation
Speaker: Siva Balakrishnan
Abstract: A classical question in statistics and in online learning, is that of model selection: given a collection of predictors, produce one which is as good as the best predictor (upto a small additive error). Variants of this basic primitive have many "applications" in theory, often yielding surprising results in new contexts. I will discuss, at a high-level, some old ideas and results in model selection aggregation, and work towards an analysis of Q-aggregation. Most of the ideas of the analysis follow from old papers -- https://arxiv.org/abs/1203.2507 and https://arxiv.org/pdf/1301.6080.pdf Some newer papers (which I won't cover) provide a new perspective and some fresh motivation -- https://arxiv.org/pdf/1803.09349.pdf and https://arxiv.org/pdf/2102.12919.pdf