TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs Minjae Lee, Wonjun Kang, Byeongkeun Ahn, Christian Classen, Kevin Galim, Seunghyuk Oh, Minghao Yan, Hyung Il Koo, and Kangwook Lee EACL 2026 (Findings)
2025
Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games Dongmin Park, Minkyu Kim, Beongjun Choi, Junhyuck Kim, Keon Lee, Jonghyun Lee, Inkyu Park, Byeong-Uk Lee, Jaeyoung Hwang, Jaewoo Ahn, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Pritam Biswas, Yoshi Suhara, Kangwook Lee, Jaewoong Cho EMNLP 2025 (Wordplay Workshop) Outstanding Paper Award
Transformers in the Dark: Navigating unknown search spaces via noisy feedback Jungtaek Kim, Ziqian Lin, Thomas Zeng, Minjae Lee, Chungpa Lee, Jy-yong Sohn, Hyung Il Koo, and Kangwook Lee NeurIPS 2025 (WCTD Workshop)
Improvement-Guided Iterative DPO for Diffusion Models Ying Fan, Fei Deng, Yang Zhao, Sahil Singla, Rahul Jain, Tingbo Hou, Kangwook Lee, Feng Yang, Deepak Ramachandran, and Qifei Wang ICML 2025 Workshop
In-batch Ensemble Drafting: Toward Fast and Robust Speculative Decoding for Multimodal Language Models Minjae Lee, Wonjun Kang, Byeongkeun Ahn, Christian Classen, Minghao Yan, Hyung Il Koo, and Kangwook Lee ICLR 2025 (SCOPE Workshop)
ENTP: Encoder-only Next Token Prediction Ethan Ewer, Daewon Chae, Thomas Zeng, Jinkyu Kim, and Kangwook Lee Transactions on Machine Learning Research (TMLR) 2025
Transformers Can Learn Meta-skills for Task Generalization in In-Context Learning Ying Fan, Steve Yadlowsky, Dimitris Papailiopoulos, and Kangwook Lee NeurIPS 2024 (Compositional Learning Workshop)
Online Federated Learning based Object Detection across Autonomous Vehicles in a Virtual World Shenghong Dai, S M Iftekharul Alam, Ravikumar Balakrishnan, Kangwook Lee, Suman Banerjee, and Nageen Himayat IEEE CCNC 2023 (Demo)
Image Clustering Conditioned on Text Criteria Sehyun Kwon, Jaeseung Park, Minkyu Kim, Jaewoong Cho, Ernest K. Ryu, and Kangwook Lee NeurIPS 2023 Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models (R0-FOMO)
Coded Prompts for Large Language Models Ziqian Lin, Yicong Chen, Yuchen Zeng, and Kangwook Lee NeurIPS 2023 Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models (R0-FOMO)
Zero-shot Improvement of Object Counting with CLIP Ruisu Zhang, Yicong Chen, and Kangwook Lee NeurIPS 2023 Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models (R0-FOMO)
The Expressive Power of Low-Rank Adaptation Yuchen Zeng and Kangwook Lee NeurIPS 2023 Workshop on Optimization for Machine Learning (OPT 2023) | Github
Teaching Arithmetic to Small Transformers Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, and Dimitris Papailiopoulos NeurIPS 2023 Workshop on Mathematical Reasoning and AI
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, and Kangwook Lee ICML 2023 Workshop on Efficient Systems for Foundation Models
Looped Transformers are Better at Learning Learning Algorithms Liu Yang, Kangwook Lee, Robert D Nowak, and Dimitris Papailiopoulos ICML 2023 Workshop on Efficient Systems for Foundation Models
A Representer Theorem for Vector-Valued Neural Networks: Insights on Weight Decay Training and Widths of Deep Neural Networks Joseph Shenouda, Rahul Parhi, Kangwook Lee, and Robert D Nowak ICML 2023 Workshop on Duality Principles for Modern Machine Learning
Teaching Arithmetic to Small Transformers Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, and Dimitris Papailiopoulos ICML 2023 Workshop on Neural Conversational AI Workshop
Looped Transformers as Programmable Computers Angeliki Giannou, Shashank Rajput, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, and Dimitris Papailiopoulos ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models
Mini-Batch Optimization of Contrastive Loss Kartik Sreenivasan, Keon Lee, Jeong-Gwan Lee, Anna Lee, Jaewoong Cho, Jy-yong Sohn, Dimitris Papailiopoulos, and Kangwook Lee ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models
Hierarchical Deep Reinforcement Learning-based Propofol Infusion Assistant Framework in Anesthesia Won Joon Yun, MyungJae Shin, David Mohaisen, Kangwook Lee, and Joongheon Kim IEEE Transactions on Neural Networks and Learning Systems, 2022
Addendum and Erratum to “The MDS Queue: Analysing the Latency Performance of Erasure Codes” Kangwook Lee, Nihar Shah, Longbo Huang, and Kannan Ramchandran IEEE Transactions on Information Theory, 2022
Super Seeds: extreme model compression by trading off storage with computation Nayoung Lee*, Shashank Rajpu*, Jy-yong Sohn, Hongyi Wang, Aliot Nagle, Eric P. Xing, Kangwook Lee, and Dimitris Papailiopoulos ICML 2022 Workshop on Updatable Machine Learning (UpML 2022)
The Roaming Edge and its Applications Suman Banerjee, Remzi Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, and Shivaram Venkataraman GetMobile: Mobile Computing and Communications, Volume 25, Issue 4, December 2021
Predicting Vehicle Collisions using Data Collected from Video Games Hoon Kim*, Kangwook Lee*, and Changho Suh Springer Machine Vision and Applications 2021
Gradient Inversion with Generative Image Prior Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, and Jungseul Ok ICML 2021 Workshop on Federated Learning for User Privacy and Data Confidentiality
Empirical Study on the Effective VC Dimension of Low-rank Neural Networks Daewon Seo, Hongyi Wang, Dimitris Papailiopoulos, and Kangwook Lee ICML 2020 Workshop on Overparameterization: Pitfalls & Opportunities
GAN-mixup: Augmenting Across Data Manifolds for Improved Robustness Jy-yong Sohn, Kangwook Lee, Jaekyun Moon, and Dimitris Papailiopoulos ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning
Improving Model Robustness via Automatically Incorporating Self-supervision Tasks Dongwha Kim, Kangwook Lee, and Changho Suh NeurIPS 2019 Workshop on Meta-Learning (MetaLearn 2019)
SGD on Random Mixtures: Private Machine Learning under Data-breach Threats Kangwook Lee, Kyungmin Lee, Hoon Kim, Changho Suh, and Kannan Ramchandran SysML 2018
Speeding Up Distributed Machine Learning Using Codes Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran IEEE Transactions on Information Theory, January 2018 The Joint Communications Society/Information Theory Society Paper Award, 2020
An Optimized Distributed Video-on-Demand Streaming System: Theory and Design (code) Kangwook Lee, Hao Zhang, Ziyu Shao, Minghua Chen, Abhay Parekh, and Kannan Ramchandran Allerton Conference on Communication, Control and Computing 2012
Codes for a Distributed Caching based Video-On-Demand System Sameer Pawar, Salim Rouayheb, Hao Zhang, Kangwook Lee, and Kannan Ramchandran Asilomar Conference on Signals, Systems, and Computers 2011
Experiment evaluation of optimal CSMA Bruno Nardelli, Jinsung Lee, Kangwook Lee, Yung Yi, Song Chong, Edward Knightly, and Mung Chiang IEEE INFOCOM 2011
Crash to not crash: Playing video games to predict vehicle collisions Kangwook Lee, Hoon Kim, and Changho Suh ICML 2017 Workshop on Machine Learning for Autonomous Vehicles
Large-scale and Interpretable Collaborative Filtering for Educational Data Kangwook Lee, Jichang Chung, and Changho Suh KDD 2017 Workshop on Advancing Education with Data
Machine Learning Approaches for Learning Analytics: Collaborative Filtering or Regression With Experts? Kangwook Lee, Jichang Chung, Youngmin Cha, and Changho Suh NIPS 2016 Workshop on Machine Learning for Education