Se-Young Yun

Proposes a framework for performing fair PCA in memory limited, streaming setting. Sample complexity results and empirical discussions show the superiority of our approach compared to the existing approaches.

A novel problem setting where heterogeneous multi-agent bandits collaborate over a network to minimize their group regret. To deal with the high communication complexity of the classic flooding protocol combined with UCB, a new network protocol called Flooding with Absorption (FwA) is proposed. Theoretical and empirical analyses are provded for flooding and FwA, showing the efficacy of our proposed FwA.

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.

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

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.

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specific topics tbd

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.

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).

(tbd)