Statistics

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.

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.

Proposes a new MMD-based definition of fairness for PCA, then formulate fair PCA as an optimization over the Stiefel manifold. Various theoretical and empirical discussions show the superiority of our approach compared to the existing approach (Olfat & Aswani, AAAI'19).

Developed least disagree metric (LDM), a new disagreement-based uncertainty measure, and an LDM-based active learning algorithm with state-of-the-art performance guarantee.

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.

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