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Policy Evaluation for Temporal and/or Spatial Dependent Experiments
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments.
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance.
Sparse Functional Principal Component Analysis in High Dimensions
Functional principal component analysis (FPCA) is a fundamental tool and has attracted increasing attention in recent decades, while existing methods are restricted to data with a single or finite number of random functions (much smaller than the sample size $n$).
Methodology
