8 citations · 15 across the 5 of their papers we have counts for
9 papers
Tractable Optimality in Episodic Latent MABs
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis +1
We consider a multi-armed bandit problem with latent contexts, where an agent interacts with the environment for an episode of time steps. Depending on the length of the ep…
Reward-Mixing MDPs with a Few Latent Contexts are Learnable
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis +1
We consider episodic reinforcement learning in reward-mixing Markov decision processes (RMMDPs): at the beginning of every episode nature randomly picks a latent reward model among…
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis +1
Motivated by online recommendation systems, we propose the problem of finding the optimal policy in multitask contextual bandits when a small fraction of tasks (users) are…
RL for Latent MDPs: Regret Guarantees and a Lower Bound
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis +1
In this work, we consider the regret minimization problem for reinforcement learning in latent Markov Decision Processes (LMDP). In an LMDP, an MDP is randomly drawn from a set of…
On the computational and statistical complexity of over-parameterized matrix sensing
Jiacheng Zhuo, Jeongyeol Kwon, Nhat Ho +1
We consider solving the low rank matrix sensing problem with Factorized Gradient Descend (FGD) method when the true rank is unknown and over-specified, which we refer to as over-pa…
On the Minimax Optimality of the EM Algorithm for Learning Two-Component Mixed Linear Regression
Jeongyeol Kwon, Nhat Ho, Constantine Caramanis
We study the convergence rates of the EM algorithm for learning two-component mixed linear regression under all regimes of signal-to-noise ratio (SNR). We resolve a long-standing q…