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 -
Calibrated Decision-Making through LLM-Assisted Retrieval
[paper]
Chaeyun Jang, Hyungi Lee, Seanie Lee and Juho Lee
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
- 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
- 2023.10, Tech. Talk, Technische Hochschule Nürnberg Georg Simon Ohm. Present Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation.
- 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
-
2024.01 - 2024.06, Internship at Mila. Host: Yoshua Bengio.
- 2023.06 - 2023.09, Internship at Apple Cambridge. Host: Anders Johannsen.
- 2022.06 - 2022.09, Remote internship at NUS. Host: Kenji Kawaguchi.