Markov Chain

Nearly Optimal Latent State Decoding in Block MDPs
Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will talk about my own paper “Nearly Optimal Latent State Decoding in Block MDPs” (Jedra et al., arXiv 2022).
Oct 7, 2022
Project
Clustering in Block Markov Chains
Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the paper “Clustering in Block Markov Chains” (Sanders et al., Ann. Stat. 2020). Abstract (taken directly from the paper)
Nov 26, 2021
Project
Near-Optimal Clustering in Mixture of Markov Chains
We study the problem of clustering T trajectories of length H, each generated by one of K unknown ergodic Markov chains over a finite …
Junghyun Lee, Yassir Jedra, Alexandre Proutière, Se-Young Yun
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Near-Optimal Clustering in Mixture of Markov Chains
On the Estimation of Linear Softmax Parametrized Markov Chains
tbd
Kunwoo Na, Junghyun Lee, Se-Young Yun
Cite Project Slides
On the Estimation of Linear Softmax Parametrized Markov Chains
Nearly Optimal Latent State Decoding in Block MDPs
First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with near-optimal upper bound are provided.
Yassir Jedra, Junghyun Lee, Alexandre Proutière, Se-Young Yun
PDF Cite Code Project Poster Slides
Nearly Optimal Latent State Decoding in Block MDPs
Statistical Learning in Structured Markov Chains and MDPs
Project #1. Clustered State Space Nearly Optimal Latent State Decoding in Block MDPs Accepted to AISTATS 2023 Joint work with Se-Young Yun (KAIST AI) and Yassir Jedra, Alexandre Proutière (KTH EECS).
Junghyun Lee