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Conference Papers

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 Track
  • Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
    T. Dinh, J. Sohn, S. Rajput, T. Ossowski, Y. Ming, J. Hu, D. Papailiopoulos and K. Lee
    Findings of EMNLP 2022 (Arxiv)
  • Score-based generative modeling secretly minimizes the Wasserstein distance
    D. Kwon, Y. Fan, and K. Lee
    NeurIPS 2022
  • LIFT: Language-Interfaced FineTuning for Non-Language Machine Learning Tasks
    T. Dinh*, Y. Zeng*, R. Zhang, Z. Lin, M. Gira, S. Rajput, J. Sohn, D. Papailiopoulos and K. Lee
    NeurIPS 2022 (Arxiv) (Github)
  • Rare Gems: Finding Lottery Tickets at Initialization
    K. Sreenivasan, J. Sohn, L. Yang, M. Grinde, A. Nagle, H. Wang, K. Lee, D. Papailiopoulos
    NeurIPS 2022 (Arxiv)
  • GenLabel: Mixup Relabeling using Generative Models
    J. Sohn, L. Shang, H. Chen, J. Moon, D. Papailiopoulos, and K. Lee
    ICML 2022 (Arxiv)
  • Breaking Fair Binary Classification with Optimal Flipping Attacks
    C. Jo, J. Sohn, and K. Lee
    ISIT 2022 (Arxiv) (Article)
  • Permutation-Based SGD: Is Random Optimal?
    S. Rajput, K. Lee, and D. Papailiopoulos
    ICLR 2022

2021

  • Sample Selection for Fair and Robust Training
    Y. Roh, K. Lee, S. Whang, and C. Suh
    NeurIPS 2021
  • Gradient Inversion with Generative Image Prior
    J. Kim, J. Jeon, K. Lee, S. Oh, and J. Ok
    NeurIPS 2021, ICML Workshop on Federated Learning 2021
  • Coded-InvNet for Resilient Prediction Serving Systems
    T. Dinh, and K. Lee
    ICML 2021 long oral
  • Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
    C. Jo, and K. Lee
    ICML 2021
  • Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification
    S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos
    MLSys 2021
  • FairBatch: Batch Selection for Model Fairness
    Y. Roh, K. Lee, S. Whang, and C. Suh
    ICLR 2021

2020

  • Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
    H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos
    NeurIPS 2020
  • Reprogramming GANs via Input Noise Design
    K. Lee, C. Suh, and K. Ramchandran
    ECML PKDD 2020
  • FR-Train: A mutual information-based approach to fair and robust training
    Y. Roh, K. Lee, S. Whang, and C. Suh
    ICML 2020

2019

  • Synthesizing Differentially Private Datasets using Random Mixing
    K. Lee, H. Kim, K. Lee, C. Suh, and K. Ramchandran
    IEEE ISIT 2019
  • Crash to Not Crash: Learn to Identify Dangerous Vehicles using a Simulator
    H. Kim*, K. Lee*, G. Hwang, and C. Suh
    AAAI 2019 long oral (Project page)

2018

  • Binary Rating Estimation with Graph Side Information
    K. Ahn, K. Lee, H. Cha, and C. Suh
    NeurIPS 2018
  • On the Joint Recovery of Community Structure and Community Features
    J. Yoon, K. Lee, and C. Suh
    Allerton Conference on Communication, Control and Computing 2018
  • Hierarchical Coding for Distributed Computing
    H. Park, K. Lee, J. Sohn, C. Suh, and J. Moon
    IEEE ISIT 2018
  • Straggler-proofing massive-scale distributed matrix multiplication with d-dimensional product codes
    T. Baharav, K. Lee, O. Ocal, and K. Ramchandran
    IEEE ISIT 2018
  • Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
    K. Lee*, H. Kim*, and C. Suh
    ICLR 2018
  • SGD on Random Mixtures: Private Machine Learning under Data-breach Threats
    K. Lee, K. Lee, H. Kim, C. Suh, and K. Ramchandran
    SysML 2018, ICLR Workshop 2018
  • UberShuffle: Communication-efficient Data Shuffling for SGD via Coding Theory
    J. Chung, K. Lee, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
    SysML 2018, NIPS Workshop on Machine Learning Systems 2017

<= 2017

  • Matrix Sparsification for Coded Matrix Multiplication
    G. Suh, K. Lee, and C. Suh
    Allerton Conference on Communication, Control and Computing 2017
  • High-Dimensional Coded Matrix Multiplication
    K. Lee, C. Suh, and K. Ramchandran
    IEEE ISIT 2017
  • Coded Computation for Multicore Setups
    K. Lee, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
    IEEE ISIT 2017
  • Information-theoretic Limits of Subspace Clustering
    K. Ahn, K. Lee, and C. Suh
    IEEE ISIT 2017
  • Asynchronous and Noncoherent Neighbor Discovery for the IoT Using Sparse-Graph Codes
    K. Chandrasekher, K. Lee, P. Kairouz, R. Pedarsani, and K. Ramchandran
    IEEE ICC 2017
  • Community Recovery in Hypergraphs
    K. Ahn, K. Lee, and C. Suh
    Allerton Conference on Communication, Control and Computing 2016
  • Speeding Up Distributed Machine Learning Using Codes
    K. Lee, M. Lam, R. Pedarsani, and K. Ramchandran
    IEEE ISIT 2016, NIPS Workshop on Machine Learning Systems 2015
  • SAFFRON: Sparse-Graph Code Framework for Group Testing
    K. Lee, R. Pedarsani, and K. Ramchandran
    IEEE ISIT 2016
  • On Scheduling Redundant Requests with Cancellation Overheads
    K. Lee, R. Pedarsani, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2015
  • Sparse Covariance Estimation Based on Sparse-Graph Codes
    R. Pedarsani, K. Lee, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2015
  • Fast and Robust Compressive Phase Retrieval with Sparse- Graph Codes
    D. Yin, K. Lee, and K. Ramchandran
    IEEE ISIT 2015
  • Capacity-Approaching PhaseCode for Low-Complexity Compressive Phase Retrieval
    R. Pedarsani, K. Lee, and K. Ramchandran
    IEEE ISIT 2015
  • PhaseCode: Fast and Efficient Compressive Phase Retrieval based on Sparse-Graph-Codes
    R. Pedarsani, K. Lee, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2014
  • The MDS Queue: Analysing the Latency Performance of Codes
    N. Shah, K. Lee, and K. Ramchandran
    IEEE ISIT 2014
  • When Do Redundant Requests Reduce Latency?
    N. Shah, K. Lee, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2013
  • A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation
    K. Lee, L. Yan, A. Parekh, and K. Ramchandran
    IEEE MASCOTS 2013
    Best Paper Award finalist
  • An Optimized Distributed Video-on-Demand Streaming System: Theory and Design
    K. Lee, H. Zhang, Z. Shao, M. Chen, A. Parekh, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2012
  • Codes for a Distributed Caching based Video-On-Demand System
    S. Pawar, S. Rouayheb, H. Zhang, K. Lee, and K. Ramchandran
    Asilomar Conference on Signals, Systems, and Computers 2011
  • Experiment evaluation of optimal CSMA
    B. Nardelli, J. Lee, K. Lee, Y. Yi, S. Chong, E. Knightly, and M. Chiang
    IEEE INFOCOM 2011

Workshop Papers

  • A Better Way to Decay: Proximal Gradient Training Algorithms for Neural Nets
    L. Yang, J. Zhang, J. Shenouda, D. Papailiopoulos, K. Lee, and R. Nowak
    NeurIPS OPT Workshop, 2022 Arxiv
  • Active Learning is a Strong Baseline for Data Subset Selection
    D. Park, D. Papailiopoulos, K. Lee
    NeurIPS HITY Workshop 2022
  • Super Seeds: extreme model compression by trading off storage with computation
    N. Lee*, S. Rajput*, J. Sohn, H. Wang, A. Nagle, E. Xing, K. Lee, D. Papailiopoulos
    ICML Workshop on Updatable Machine Learning (UpML 2022)
  • Improved Input Reprogramming for GAN Conditioning
    T. Dinh, D. Seo, Z. Du, L. Shang, and K. Lee
    ICML Workshop on Updatable Machine Learning (UpML 2022) Arxiv
  • Improving Fairness via Federated Learning
    Y. Zeng, H. Chen, and K. Lee
    MLSys-CrossFL 2022
    AAAI Workshop on Federated Learning 2022
  • Dynamic Decentralized Federated Learning
    S. Dai, K. Lee, and S. Banerjee
    MLSys-CrossFL 2022
  • Debiasing Pre-Trained Language Models via Efficient Fine-tuning
    M. Gira, R. Zhang, and K. Lee
    ACL Workshop on Language Technology for Equality, Diversity, Inclusion 2022
  • Federated Unsupervised Clustering with Generative Models
    J. Chung, K. Lee, and K. Ramchandran
    AAAI Workshop on Federated Learning 2022
  • Empirical Study on the Effective VC Dimension of Low-rank Neural Networks
    D. Seo, H. Wang, D. Papailiopoulos, and K. Lee
    ICML Workshop on Overparameterization: Pitfalls & Opportunities 2021
  • GAN-mixup: Augmenting Across Data Manifolds for Improved Robustness
    J. Sohn, K. Lee, J. Moon, and D. Papailiopoulos
    ICML Workshop on Uncertainty & Robustness in Deep Learning 2020
  • Improving Model Robustness via Automatically Incorporating Self-supervision Tasks
    D. Kim, K. Lee, and C. Suh
    NeurIPS Workshop on Meta-Learning (MetaLearn 2019) 2019
  • Crash to not crash: Playing video games to predict vehicle collisions
    K. Lee, H. Kim, and C. Suh
    ICML Workshop on Machine Learning for Autonomous Vehicles 2017
  • Large-scale and Interpretable Collaborative Filtering for Educational Data
    K. Lee, J. Chung, and C. Suh
    KDD Workshop on Advancing Education with Data 2017
  • Machine Learning Approaches for Learning Analytics: Collaborative Filtering or Regression With Experts?
    K. Lee, J. Chung, Y. Cha, and C. Suh
    NIPS Workshop on Machine Learning for Education 2016