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Generalized Linear Bandits with Memory

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Heesang Ann, Hyunjun Choi, Taehyun Hwang, Younghoon Shin, Haeju Cheong, Min-hwan Oh

Diversified Multinomial Logit Contextual Bandits

Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of …

Heesang Ann, Taehyun Hwang, Min-hwan Oh

Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities

We study the multinomial logit (MNL) contextual bandit problem for sequential assortment selection. Although most existing research …

Taehyun Hwang, Dahngoon Kim, Min-hwan Oh

Lasso Bandit with Compatibility Condition on Optimal Arm

We consider a stochastic sparse linear bandit problem where only a sparse subset of context features affects the expected reward …

Harin Lee, Taehyun Hwang, Min-hwan Oh

Combinatorial Neural Bandits

We consider a contextual combinatorial bandit problem where in each round a learning agent selects a subset of arms and receives …

Taehyun Hwang, Kyuwook Chai, Min-hwan Oh

Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation

We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is …

Taehyun Hwang, Min-hwan Oh