Se-Young Yun

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

Nearly Optimal Latent State Decoding in Block MDPs

Preliminary Empirical Analyses of Clustering in Block MDPs

We empirically validate the clustering algorithm proposed in (Jedra et al., 2022).

Junghyun Lee, Se-Young Yun

A Statistical Analysis of Stochastic Gradient Noises for GNNs

Inspired from (Wang et al., ICLR'22), we provide a preliminary statistical analysis of stochastic gradient noises (SGNs) of GIN and GCN in Cora node classification task.

Junghyun Lee, Minchan Jeong, Namgyu Ho, Se-Young Yun

(Statistically/Theoretically) Principled Approaches to LLM Reasoning

Junghyun Lee

Collaborative Multi-Agent Bandits

specific topics tbd

Junghyun Lee

Fair Dimensionality Reduction

Part of fair representation learning. Develop a theory of fairness in dimensionality reduction: new definition, new (efficient) algorithm, new theoretical results. Currently focused on PCA.

Junghyun Lee

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

Statistical Problems Related to (LLM) Alignment and Preference Learning

Junghyun Lee