Reading Seminar

We hold SDS reading seminars irregularly, depending on our schedules.
Speakers are primarily students, and they give a talk about recent papers or general topics in statistics and data science.

2022

  • [Jun 30] Facebook Prophet Model
    Presented by Hyeokkyu Kwon. [Slides]

  • [Jun 22] Bayesian Distributionally Robust Optimization
    Presented by Seonghwi Kim. [Slides]

  • [Fed 23] GPU-Powered Shotgun Stochastic Search for Dirichlet Process Mixtures of Gaussian Graphical Models
    Presented by Seonghwi Kim. [Slides]

  • [Fed 9] Next Item Recommendation with Self-Attention
    Presented by Yejin Kim. [Slides]

  • [Jan 26] Unbiased Markov Chain Monte Carlo Methods with Couplings
    Presented by Jeyong Lee. [Slides]

  • [Jan 26] Spike-and-Slab Prior in Bayesian Neural Networks
    Presented by Junhyeok Choi. [Slides]

  • [Jan 19] A Deep Generative Approach to Conditional Sampling
    Presented by Hyeokkyu Kwon. [Slides]

  • [Jan 4] Causal Inference in Statistics

    • Statistical and Causal Models
      Presented by Seonghwi Kim. [Slides]

    • Graphical Models and Their applications
      Presented by Junhyeok Choi. [Slides]

    • The Effects of Interventions
      Presented by Hyeokkyu Kwon. [Slides]

    • Counterfactuals and Their Applications
      Presented by Dongguen Kim. [Slides]

2021

  • [Aug 25] Auto-encoding Variational Bayes in Topic Model
    Presented by Seonghwi Kim. [Slides]

  • [Aug 25] Self-Attentive Sequential Recommendation
    Presented by Heejin Kim. [Slides]

  • [Aug 18] Style GAN
    Presented by Hyeokkyu Kwon. [Slides]

  • [Aug 11] Bayesian Neural Network
    Presented by Junhyeok Choi. [Slides]

  • [Aug 11] Abstractive Summarization
    Presented by Yejin Kim. [Slides]

  • [Aug 4] Wasserstein GAN
    Presented by Hyeokkyu Kwon. [Slides]

  • [Jul 21] Theoretical Foundation of t-SNE
    Presented by Jeyong Lee. [Slides]

  • [Jul 14] Zero Shot Learning
    Presented by Junhyeok Choi. [Slides]

  • [Jul 7] Introduction to Language Modeling
    Presented by Yejin Kim. [Slides]

  • [Jul 7] Markov Decision Process
    Presented by Dongguen Kim. [Slides]

  • [Jun 30] Introduction to Reinforcement Learning
    Presented by Dongguen Kim. [Slides]

  • [Jun 23] Manifold Learning
    Presented by Hyeokkyu Kwon. [Slides]

  • [Feb 24] Evaluation metric for generative model
    Presented by Hyeokkyu Kwon. [Slides]

2020

  • [Sep 2] Variational Auto-Encoder
    Presented by Hyeokkyu Kwon. [Slides]

  • [Aug 26] Bayesian Optimization
    Presented by Junhyeok Choi. [Slides]

  • [Aug 26] t-SNE
    Presented by Heejin Kim. [Slides]

  • [Aug 26] How does batch normalization help optimization?
    Presented by Dongguen Kim. [Slides]

  • [Aug 12] Natural Language Processing (Word Embedding)
    Presented by Seonghwi Kim. [Slides]

  • [Aug 5] ResNet
    Presented by Yejin Kim. [Slides]

  • [Aug 5] Large Sparse Linear System
    Presented by Jeyong Lee. [Slides]

  • [Jul 29] Model Assessment and Selection
    Presented by Jeyong Lee. [Slides]

  • [Jul 29] Dropout & Batch Normalization
    Presented by Dongguen Kim. [Slides]

  • [Jul 22] Transfer Learning
    Presented by Hyeokkyu Kwon. [Slides]

  • [Jul 22] Object Detection
    Presented by Heejin Kim. [Slides]

  • [Jul 15] Optimizer
    Presented by Junhyeok Choi. [Slides]

  • [Jul 15] Latent Dirichlet Allocation
    Presented by Seonghwi Kim. [Slides]

  • [Jul 9] GPU and CPU
    Presented by Jongwon Kim. [Slides]