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Publication

Preprints

  • 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
    Preprint (Arxiv)

Journal Papers

  • Hierarchical Deep Reinforcement Learning-based Propofol Infusion Assistant Framework in Anesthesia
    WJ. Yun, MJ. Shin, D. Mohaisen, K. Lee, and J. Kim
    IEEE Transactions on Neural Networks and Learning Systems, 2022
  • Addendum and Erratum to “The MDS Queue: Analysing the Latency Performance of Erasure Codes”
    K. Lee, N. Shah, L. Huang, and K. Ramchandran
    IEEE Transactions on Information Theory, 2022
  • The Roaming Edge and its Applications
    S. Banerjee, R. Arpaci-Dusseau, S. Dai, K. Fawaz, M. Gupta, K. Lee, S. Venkataraman
    GetMobile: Mobile Computing and Communications, Volume 25, Issue 4, December 2021
  • Predicting Vehicle Collisions using Data Collected from Video Games
    H. Kim*, K. Lee*, G. Hwang, and C. Suh
    Springer Machine Vision and Applications 2021
  • SAFFRON: Sparse-Graph Code Framework for Group Testing
    K. Lee, K. Chandrasekher, R. Pedarsani, and K. Ramchandran
    IEEE Transactions on Signal Processing 2019
  • Community Recovery in Hypergraphs
    K. Ahn*, K. Lee*, and C. Suh
    IEEE Transactions on Information Theory 2019
  • Hypergraph Spectral Clustering in the Weighted Stochastic Block Model
    K. Ahn, K. Lee, and C. Suh
    IEEE Journal of Selected Topics in Signal Processing, October 2018
  • Speeding Up Distributed Machine Learning Using Codes
    K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
    IEEE Transactions on Information Theory, January 2018
    The Joint Communications Society/Information Theory Society Paper Award, 2020
  • PhaseCode: Fast and Efficient Compressive Phase Retrieval based on Sparse-Graph-Codes
    R. Pedarsani, D. Yin, K. Lee, and K. Ramchandran
    IEEE Transactions on Information Theory, June 2017
  • The MDS Queue: Analysing the Latency Performance of Erasure Codes
    K. Lee, N. Shah, L. Huang, and K. Ramchandran
    IEEE Transactions on Information Theory, May 2017
  • On Scheduling Redundant Requests With Cancellation Overheads
    K. Lee, R. Pedarsani, and K. Ramchandran
    IEEE/ACM Transactions on Networking, April 2017
  • When Do Redundant Requests Reduce Latency?
    N. Shah, K. Lee, and K. Ramchandran
    IEEE Transactions on Communications, February 2016

Peer-reviewed Conference Papers

2022

  • 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)
  • 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
  • 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

Peer-reviewed Workshop Papers

  • 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