Professor Myunghee Cho Paik

Department of Statistics

Seoul National University

Teaching

  • Fall 2020 Deep Learning: statistical perspective
  • Spring 2021 Sequential Decision Making
  • Recent manuscripts

  • Valid oversampling schemes to handle imbalance published in the Pattern Recognition Letters, 2019
  • Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation published in the CSDA, 2020
  • Lipschitz continuous autoencoders in application to anomaly detection presented at AISTATS 2020
  • Contextual multi-armed bandit algorithm for semiparametric reward model presented at ICML 2019
  • Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric published in the Machine Learning, 2020
  • Doubly Robust Lasso Bandit presented at NeurIPs 2019
  • Principled Learning Method for Wasserstein Distributionally Robust Optimization with Local Perturbations presented at ICML 2020
  • Kernel-convoluted Deep Neural Networks with Data Augmentation presented at AAAI 2021