In combinatorial causal bandits (CCB), the learning agent chooses at most K variables in each round to intervene, collects feedback from observed variables, with goal of minimizing expected regret on target variable Y. We study under context binary generalized linear models (BGLMs) a succinct parametric representation models. present algorithm BGLM-OFU for Markovian BGLMs (i.e., no hidden varia...

Assortment optimization refers to the problem of designing a slate products offer potential customers, such as stocking shelves in convenience store. The price each product is fixed advance, and probabilistic choice function describes which customer will choose from any given subset. We introduce combinatorial assortment problem, where may select bundle products. consider model consumer selects...

We prove that the homotopy theory of $N_\infty$ operads is equivalent to a discrete operads, and we construct free associative operadic realizations every indexing system. This resolves conjecture Blumberg Hill in affirmative.