3 accepted papers
We study bilinear bandits with partially observable features on both the user and item sides. In each of $T$ rounds, the learner selects an arm and observes only the reward for the chosen pair. The reward model is linear in the user and item features with an unknown parameter matrix. Existing litera…
We study reinforcement learning with _multinomial logistic_ (MNL) function approximation where the underlying transition probability kernel of the _Markov decision processes_ (MDPs) is parametrized by an unknown transition core with features of state and action. For the finite horizon episodic setti…
Contextual dynamic pricing is a problem of setting prices based on current contextual information and previous sales history to maximize revenue. A popular approach is to postulate a distribution of customer valuation as a function of contextual information and the baseline valuation. A semi-paramet…