Link Search Menu Expand Document

Conference Papers

2024

  • 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

2022

2021

2020

2019

2018

<= 2017

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