Conference Papers
2024
- Can MLLMs Perform Text-to-Image In-Context Learning?
Yuchen Zeng*, Wonjun Kang*, Yicong Chen, Hyung Il Koo, and Kangwook Lee
COLM 2024 | Summary | Github - Dual Operating Modes of In-Context Learning
Ziqian Lin and Kangwook Lee
ICML 2024 | Summary | Github - Can Mamba Learn How To Learn? A Comparative Study on In-Context Learning Tasks
Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
ICML 2024 | Summary | Github - Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks
Jy-yong Sohn, Dohyun Kwon, Seoyeon An, Kangwook Lee
UAI 2024 | Summary | Github - The Expressive Power of Low-Rank Adaptation
Yuchen Zeng and Kangwook Lee
ICLR 2024 | Summary | Github - Image Clustering Conditioned on Text Criteria
Sehyun Kwon, Jaeseung Park, Minkyu Kim, Jaewoong Cho, Ernest K. Ryu, and Kangwook Lee
ICLR 2024 | Summary | Github - Teaching Arithmetic to Small Transformers
Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, and Dimitris Papailiopoulos
ICLR 2024 | Summary | Github - Looped Transformers are Better at Learning Learning Algorithms
Liu Yang, Kangwook Lee, Robert D Nowak, and Dimitris Papailiopoulos
ICLR 2024 | Summary | Github
2023
- DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models (code)
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee
NeurIPS 2023 - Prompted LLMs as Chatbot Modules for Long Open-domain Conversation (code)
Gibbeum Lee, Volker Hartmann, Jongho Park, Dimitris Papailiopoulos, and Kangwook Lee
ACL 2023 (Findings, Short) - Improving Fair Training under Correlation Shifts
Yuji Roh, Kangwook Lee, Steven Euijong Whang, Changho Suh
ICML 2023 - Optimizing DDPM Sampling with Shortcut Fine-Tuning (code)
Ying Fan, Kangwook Lee
ICML 2023 - Looped Transformers as Programmable Computers (code)
Angeliki Giannou*, Shashank Rajput*, Jy-yong Sohn, Kangwook Lee, Jason D. Lee, Dimitris Papailiopoulos
ICML 2023 - Federated Learning with Local Fairness Constraints
Yuchen Zeng, Hongxu Chen, and Kangwook Lee
ISIT 2023 - Equal Improvability: A New Fairness Notion Considering the Long-Term Impact (code)
Ozgur Guldogan*, Yuchen Zeng*, Jy-yong Sohn, Ramtin Pedarsani, and Kangwook Lee
ICLR 2023 (Article)
2022
- 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) - Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment (code)
Tuan Dinh, Jy-yong Sohn, Shashank Rajput, Tim Ossowski, Yifei Ming, Junjie Hu, Dimitris Papailiopoulos, and Kangwook Lee
EMNLP 2022 (Findings) - Score-based generative modeling secretly minimizes the Wasserstein distance (code)
Dohyun Kwon, Ying Fan, and Kangwook Lee
NeurIPS 2022 - LIFT: Language-Interfaced FineTuning for Non-Language Machine Learning Tasks (code)
Tuan Dinh*, Yuchen Zeng*, Ruisu Zhang, Ziqian Lin, Michael Gira, Shashank Rajput, Jy-yong Sohn, Dimitris Papailiopoulos, and Kangwook Lee
NeurIPS 2022 - Rare Gems: Finding Lottery Tickets at Initialization (code)
Kartik Sreenivasan, Jy-yong Sohn, Liu Yang, Matthew Grinde, Aliot Nagle, Hongyi Wang, Kangwook Lee, Dimitris Papailiopoulos
NeurIPS 2022 - GenLabel: Mixup Relabeling using Generative Models (code)
Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, and Kangwook Lee
ICML 2022 - Breaking Fair Binary Classification with Optimal Flipping Attacks
Changhun Jo, Jy-yong Sohn, and Kangwook Lee
ISIT 2022 (Article) - Permutation-Based SGD: Is Random Optimal?
Shashank Rajput, Kangwook Lee, and Dimitris Papailiopoulos
ICLR 2022
2021
- Sample Selection for Fair and Robust Training (code)
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh
NeurIPS 2021 - Gradient Inversion with Generative Image Prior (code)
Jinwoo Jeon, Jaechang Kim, Kangwook Lee, Sewoong Oh, and Jungseul Ok
NeurIPS 2021 - Coded-InvNet for Resilient Prediction Serving Systems (code)
Tuan Dinh and Kangwook Lee
ICML 2021 (long oral) - Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
Changhun Jo and Kangwook Lee
ICML 2021 - Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification (code)
Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, and Dimitris Papailiopoulos
MLSys 2021 - FairBatch: Batch Selection for Model Fairness (code)
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh
ICLR 2021
2020
- Attack of the Tails: Yes, You Really Can Backdoor Federated Learning (code)
Hongyi Wang, Kartik Sreenivasan, Shashank Rajpu, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos
NeurIPS 2020 - Reprogramming GANs via Input Noise Design (code)
Kangwook Lee, Changho Suh, and Kannan Ramchandran
ECML PKDD 2020 - FR-Train: A mutual information-based approach to fair and robust training (code)
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh
ICML 2020
2019
- Synthesizing Differentially Private Datasets using Random Mixing
Kangwook Lee, Hoon Kim, Kyungmin Lee, Changho Suh, and Kannan Ramchandran
IEEE ISIT 2019 - Crash to Not Crash: Learn to Identify Dangerous Vehicles using a Simulator (site)
Hoon Kim*, Kangwook Lee*, Gyeongjo Hwang, and Changho Suh
AAAI 2019 long oral
2018
- Binary Rating Estimation with Graph Side Information
Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, and Changho Suh
NeurIPS 2018 - On the Joint Recovery of Community Structure and Community Features
Jisang Yoon, Kangwook Lee, and Changho Suh
Allerton Conference on Communication, Control and Computing 2018 - Hierarchical Coding for Distributed Computing
Hyegyeong Park, Kangwook Lee, Jy-yong Sohn, Changho Suh, and Jaekyun Moon
IEEE ISIT 2018 - Straggler-proofing massive-scale distributed matrix multiplication with d-dimensional product codes
Tavor Baharav, Kangwook Lee, Orhan Ocal, and Kannan Ramchandran
IEEE ISIT 2018 - Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
Kangwook Lee*, Hoon Kim*, and Changho Suh
ICLR 2018 - SGD on Random Mixtures: Private Machine Learning under Data-breach Threats
Kangwook Lee, Kyungmin Lee, Hoon Kim, Changho Suh, and Kannan Ramchandran
SysML 2018 - UberShuffle: Communication-efficient Data Shuffling for SGD via Coding Theory
Jichang Chung, Kangwook Lee, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran*
SysML 2018
<= 2017
- Matrix Sparsification for Coded Matrix Multiplication
Geewon Suh, Kangwook Lee, and Changho Suh
Allerton Conference on Communication, Control and Computing 2017 - High-Dimensional Coded Matrix Multiplication
Kangwook Lee, Changho Suh, and Kannan Ramchandran
IEEE ISIT 2017 - Coded Computation for Multicore Setups
Kangwook Lee, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran
IEEE ISIT 2017 - Information-theoretic Limits of Subspace Clustering
Kwangjun Ahn, Kangwook Lee, and Changho Suh
IEEE ISIT 2017 - Asynchronous and Noncoherent Neighbor Discovery for the IoT Using Sparse-Graph Codes
Kabir Chandrasekher, Kangwook Lee, Peter Kairouz, Ramtin Pedarsani, and Kannan Ramchandran*
IEEE ICC 2017 - Community Recovery in Hypergraphs
Kwangjun Ahn, Kangwook Lee, and Changho Suh
Allerton Conference on Communication, Control and Computing 2016 - Speeding Up Distributed Machine Learning Using Codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran*
IEEE ISIT 2016 - SAFFRON: Sparse-Graph Code Framework for Group Testing
Kangwook Lee, Ramtin Pedarsani, and Kannan Ramchandran
IEEE ISIT 2016 - On Scheduling Redundant Requests with Cancellation Overheads
Kangwook Lee, Ramtin Pedarsani, and Kannan Ramchandran
Allerton Conference on Communication, Control and Computing 2015 - Sparse Covariance Estimation Based on Sparse-Graph Codes
Ramtin Pedarsani, Kangwook Lee, and Kannan Ramchandran
Allerton Conference on Communication, Control and Computing 2015 - Fast and Robust Compressive Phase Retrieval with Sparse-Graph Codes
Dong Yin, Kangwook Lee, Ramtin Pedarsani, and Kannan Ramchandran
IEEE ISIT 2015 - Capacity-Approaching PhaseCode for Low-Complexity Compressive Phase Retrieval
Ramtin Pedarsani, Kangwook Lee, and Kannan Ramchandran
IEEE ISIT 2015 - PhaseCode: Fast and Efficient Compressive Phase Retrieval based on Sparse-Graph-Codes
Ramtin Pedarsani, Kangwook Lee, and Kannan Ramchandran
Allerton Conference on Communication, Control and Computing 2014 - The MDS Queue: Analysing the Latency Performance of Codes
Nihar Shah, Kangwook Lee, and Kannan Ramchandran
IEEE ISIT 2014 - When Do Redundant Requests Reduce Latency?
Nihar Shah, Kangwook Lee, and Kannan Ramchandran
Allerton Conference on Communication, Control and Computing 2013 - A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation (code)
Kangwook Lee, Lisa Yan, Abhay Parekh, and Kannan Ramchandran
IEEE MASCOTS 2013 Best Paper Award finalist - 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
Workshop Papers
- 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 [Github]
Yuchen Zeng and Kangwook Lee
NeurIPS 2023 Workshop on Optimization for Machine Learning (OPT 2023) - Teaching Arithmetic to Small Transformers
Nayoung Lee, Kartik Sreenivasan, Jason Lee, Kangwook Lee, and Dimitris Papailiopoulos
NeurIPS 2023 Workshop on Mathematical Reasoning and AI - Outlier-Robust Group Inference via Gradient Space Clustering
Yuchen Zeng, Kristjan Greenewald, Luann Jung, Kangwook Lee, Justin Solomon, Mikhail Yurochkin
NeurIPS 2023 Workshop on Distribution Shifts (DistShift) - Super-Resolution Emulation of Large Cosmological Fields with a 3D Conditional Diffusion Model
Adam Rouhiainen, Michael Gira, Gary Shiu, Kangwook Lee, and Moritz Münchmeyer
NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences - 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 - FedGP: Buffer-based Gradient Projection for Continual Federated Learning
Shenghong Dai, Bryce Yicong Chen, Jy-yong Sohn, S M Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, Kangwook Lee
MLSys-FLSys 2023 Best Paper Award - 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 - Active Learning is a Strong Baseline for Data Subset Selection
Dongmin Park, Dimitris Papailiopoulos, and Kangwook Lee
NeurIPS 2022 HITY Workshop - A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets
Liu Yang, Jifan Zhang, Joseph Shenouda, Dimitris Papailiopoulos, Kangwook Lee, and Robert D. Nowak
NeurIPS 2022 OPT Workshop - 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) - Improved Input Reprogramming for GAN Conditioning
Tuan Dinh, Daewon Seo, Zhixu Du, Liang Shang, and Kangwook Lee
ICML 2022 Workshop on Updatable Machine Learning (UpML 2022) - Improving Fairness via Federated Learning
Yuchen Zeng, Hongxu Chen, and Kangwook Lee
MLSys-CrossFL 2022 - Dynamic Decentralized Federated Learning
Shenghong Dai, Kangwook Lee, and Suman Banerjee
MLSys-CrossFL 2022 - Debiasing Pre-Trained Language Models via Efficient Fine-tuning
Michael Gira, Ruisu Zhang, and Kangwook Lee
ACL 2022 Workshop on Language Technology for Equality, Diversity, Inclusion - Federated Unsupervised Clustering with Generative Models
Jichang Chung, Kangwook Lee, and Kannan Ramchandran
AAAI 2022 Workshop on Federated Learning - Improving Fairness via Federated Learning
Yuchen Zeng, Hongxu Chen, and Kangwook Lee
AAAI 2022 Workshop on Federated Learning - 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
ICLR 2018 Workshop - UberShuffle: Communication-efficient Data Shuffling for SGD via Coding Theory
Jichang Chung, Kangwook Lee, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran*
NIPS 2017 Workshop on Machine Learning Systems - 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 - Speeding Up Distributed Machine Learning Using Codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, and Kannan Ramchandran*
NIPS 2015 Workshop on Machine Learning Systems