From the 1 of 5 linked papers with an AI index.
5 papers
Diversified Multinomial Logit Contextual Bandits
Heesang Ann, Taehyun Hwang, Min-hwan Oh
The paper introduces a diversified multinomial logit (DMNL) contextual bandit model that combines relevance-driven choice with a submodular diversity term, and proposes a white‑box…
Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
Taehyun Hwang, Dahngoon Kim, Min-hwan Oh
We study the multinomial logit (MNL) contextual bandit problem for sequential assortment selection. Although most existing research assumes utility functions to be linear in item f…
Lasso Bandit with Compatibility Condition on Optimal Arm
Harin Lee, Taehyun Hwang, Min-hwan Oh
We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward function, i.e., the unknown reward parameter has a…
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
Taehyun Hwang, Min-hwan Oh
We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with featur…
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
Wooseong Cho, Taehyun Hwang, Joongkyu Lee +1
We study reinforcement learning with multinomial logistic (MNL) function approximation where the underlying transition probability kernel of the Markov decision processes (MDPs) is…