I am a PhD student at Machine Learning and Artificial Intelligence (MLAI) lab in KAIST, working on meta-learning and machine learning. I am fortunate to be advised by Professor Sung Ju Hwang and Juho Lee. I also closely collaborate with Kenji Kawaguchi. My research is graciously supported by the Apple Scholars in AI Fellowship and I am mentored by Anders Johannsen. Here is my cv.

🔥 News

• 2023.09:  🎉🎉 One paper accepted to NeurIPS 2023.
• 2023.04:  🎉🎉 Two papers accepted to ICML 2023.
• 2023.03:  🎉🎉 Selected as a 2023 recipient of the Apple Scholars in AI ML PhD fellowship.
• 2023.01:  🎉🎉 Two papers accepted to ICLR 2023.
• 2022.10:  ✈️ Google Travel Grant for NeurIPS 2022 from Google.
• 2022.09:  🎉🎉 Two papers accepted to NeurIPS 2022.
• 2022.05:  🎉🎉 One paper accepted to ICML 2022.

📝 Publications

• Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks

  [paper]
  Minki Kang, Seanie Lee, Jinheon Baek, Kenji Kawaguchi and Sung Ju Hwang
  NeurIPS 2023

• DiffusionNAG: Task-guided Neural Architecture Generation with Diffusion Models

  [paper]
  Sohyun Ahn* Hayeon Lee*, Jaehyeong Jo, Seanie Lee and Sung Ju Hwang (*: equal contribution)
  Arxiv 2023

• Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation

  [paper]
  Jeffrey Willette* Seanie Lee*, Bruno Andreis, Kenji Kawaguchi, Juho Lee and Sung Ju Hwang (*: equal contribution)
  ICML 2023

• Margin-based Neural Network Watermarking

  [paper]
  Byungjoo Kim, Suyoung Lee, Seanie Lee, Sooel Son and Sung Ju Hwang
  ICML 2023

• Self-Distillation for Further Pre-training of Transformers

  [paper]
  Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang and Kenji Kawaguchi
  ICLR 2023

• Self-Supervised Set Representation Learning for Unsupervised Meta-Learning

  [paper]
  Dong Bok Lee*, Seanie Lee*, Kenji Kawaguchi, Yunji Kim, Jihwan Bang, Jung-Woo Ha and Sung Ju Hwang (*: equal contribution)
  ICLR 2023

• Set-based Meta-Interpolation for Few-Task Meta-Learning

  [paper]
  Seanie Lee*, Bruno Andreis*, Kenji Kawaguchi, Juho Lee and Sung Ju Hwang (*: equal contribution)
  NeurIPS 2022

• On Divergence Measures for Bayesian Pseudocoresets

  [paper]
  Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha and Juho Lee
  NeurIPS 2022

• Set Based Stochastic Subsampling

  [paper]
  Bruno Andreis, Seanie Lee, A. Tuan Nguyen, Juho Lee, Eunho Yang and Sung Ju Hwang
  ICML 2022

• Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning

  [paper]
  Seanie Lee*, Hae Beom Lee*, Juho Lee and Sung Ju Hwang (*: equal contribution)
  ICLR 2022

• Learning to Perturb Word Embeddings for Out-of-distribution QA

  [paper]
  Seanie Lee*, Minki Kang*, Juho Lee and Sung Ju Hwang (*: equal contribution)
  ACL 2021

• Contrastive Learning with Adversarial Perturbations for Conditional Text Generation

  [paper]
  Seanie Lee*, Dong Bok Lee* and Sung Ju Hwang (*: equal contribution)
  ICLR 2021

• Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

  [paper]
  Dong Bok Lee, Dongchan Min, Seanie Lee and Sung Ju Hwang
  ICLR 2021

• Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs

  [paper]
  Dong Bok Lee*, Seanie Lee*, WooTae Jeong, Donghwan Kim and Sung Ju Hwang (*: equal contribution)
  ACL 2020

• g2pM: A Neural Grapheme-to-Phoneme Conversion Package for Mandarin Chinese Based on a New Open Benchmark Dataset

  [paper]
  Kyubyong Park* and Seanie Lee* (*: equal contribution)
  INTERSPEECH 2020

🎖 Honors and Awards

• 2023.03 A 2023 recipient of the Apple Scholars in AI ML PhD fellowship.
• Google Travel Grant for NeurIPS 2022 from Google
• 2018.12 Silver Medal in NLP challenge.

📖 Educations

• 2022.03 - present, PhD. in Artificial Intelligence. Korea Advanced Institute of Science and Technology.
• 2020.03 - 2022.02, M.S. in Artificial Intelligence. Korea Advanced Institute of Science and Technology.
• 2011.03 - 2018.02, B.A. in Library and Information Science. Yonsei University.

💬 Invited Talks

• 2023.05, Tech. Talk, Samsung SDS. Present Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation.
• 2021.12, Tech. Talk, NAVER corp. Present ACL 2020 paper.

💻 Internships

• 2023.06- 2023.09, Internship at Apple Cambridge. Host: Anders Johannsen.
• 2022.06 - 2022.09, Remote internship at NUS. Host: Kenji Kawaguchi.