Seanie Lee (이신의)

Seanie Lee (이신의)

PhD. Student at KAIST

I am a PhD student at Machine Learning and Artificial Intelligence (MLAI) lab in KAIST, working on Large Language Models and GFlowNet. 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. Here is my cv.

During my PhD study, I interned at Mila in 2024, where I had the opportunity to work with Yoshua Bengio, Minsu Kim, Moksh Jain, and Kolya Malkin. In 2023, I completed an internship at Apple Cambridge, working with Anders Johannsen and Jianpeng Cheng. In 2022, I did remote internship at NUS, working with Kenji Kawaguchi.

🔥 News

  • 2024.05:  🎉🎉 One paper accepted to ICML 2024.
  • 2024.03:  🎉🎉 One paper accepted to NAACL 2024.
  • 2024.01:  🎉🎉 Two papers accepted to ICLR 2024.
  • 2023.09:  🎉🎉 I got an internship offer from Mila, supervised by Yoshua Bengio.
  • 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

  • HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models

    [paper]
    Seanie Lee*, Haebin Seong*, Dong Bok Lee, Minki Kang, Xiaoyin Chen, Dominik Wagner, Yoshua Bengio, Juho Lee and Sung Ju Hwang (*: equal contribution)
    arXiv 2024

  • Learning Diverse Attacks on Large Language Models for Robust Red-teaming and Safety Tuning

    [paper]
    Seanie Lee, Minsu Kim, Lynn Cherif, David Dobre, Juho Lee, Sung Ju Hwang, Kenji Kawaguchi, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin and Moksh Jain
    arXiv 2024

  • Optimized Speculative Sampling for GPU Hardware Accelerators

    [paper]
    Dominik Wagner, Seanie Lee, Ilja Baumann, Philipp Seeberger, Korbinian Riedhammer and Tobias Bocklet
    EMNLP 2024

  • Drug Discovery with Dynamic Goal-aware Fragments

    [paper]
    Seul Lee, Seanie Lee, Kenji Kawaguchi and Sung Ju Hwang
    ICML 2024

  • Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries

    [paper]
    Seanie Lee, Jianpeng Cheng, Joris Driesen, Alexandru Coca and Anders Johannsen
    NAACL 2024

  • Self-Supervised Dataset Distillation for Transfer Learning

    [paper]
    Dong Bok Lee*, Seanie Lee*, Joonho Ko, Kenji Kawaguchi, Juho Lee and Sung Ju Hwang (*: equal contribution)
    ICLR 2024

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

    [paper]
    Sohyun Ahn* Hayeon Lee*, Jaehyeong Jo, Seanie Lee and Sung Ju Hwang (*: equal contribution)
    ICLR 2024

  • 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

  • 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

📖 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

💻 Internships