Preprints

  • [P01] Predicting Vehicle Collisions using Data Collected from Video Games
    H. Kim*, K. Lee*, G. Hwang, and C. Suh
    Under review

Journal Papers

  • [J01] SAFFRON: Sparse-Graph Code Framework for Group Testing
    K. Lee, K. Chandrasekher, R. Pedarsani, and K. Ramchandran
    IEEE Transactions on Signal Processing

  • [J02] Community Recovery in Hypergraphs
    K. Ahn*, K. Lee*, and C. Suh
    IEEE Transactions on Information Theory

  • [J03] 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

  • [J04] 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

  • [J05] 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

  • [J06] 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

  • [J07] On Scheduling Redundant Requests With Cancellation Overheads
    K. Lee, R. Pedarsani, and K. Ramchandran
    IEEE/ACM Transactions on Networking, April 2017

  • [J08] When Do Redundant Requests Reduce Latency?
    N. Shah, K. Lee, and K. Ramchandran
    IEEE Transactions on Communications, February 2016

Peer-reviewed Conference/Workshop Proceedings

  • [C01] Improving Model Robustness via Automatically Incorporating Self-supervision Tasks
    D. Kim, K. Lee, and C. Suh
    NeurIPS 2019 Workshop on Meta-Learning (MetaLearn 2019) 2019

  • [C02] Synthesizing Differentially Private Datasets using Random Mixing
    K. Lee, H. Kim, K. Lee, C. Suh, and K. Ramchandran
    IEEE ISIT 2019

  • [C03] Crash to Not Crash: Learn to Identify Dangerous Vehicles using a Simulator
    H. Kim*, K. Lee*, G. Hwang, and C. Suh
    AAAI 2019 (oral presentation) Project website

  • [C04] Binary Rating Estimation with Graph Side Information
    K. Ahn, K. Lee, H. Cha, and C. Suh
    NeurIPS 2018

  • [C05] 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

  • [C06] Hierarchical Coding for Distributed Computing
    H. Park, K. Lee, J. Sohn, C. Suh, and J. Moon
    IEEE ISIT 2018

  • [C07] Straggler-proofing massive-scale distributed matrix multiplication with d-dimensional product codes
    T. Baharav, K. Lee, O. Ocal, and K. Ramchandran
    IEEE ISIT 2018

  • [C08] Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings
    K. Lee*, H. Kim*, and C. Suh
    ICLR 2018

  • [C09] SGD on Random Mixtures: Private Machine Learning under Data-breach Threats
    K. Lee, K. Lee, H. Kim, C. Suh, and K. Ramchandran
    ICLR Workshop, SysML 2018

  • [C10] 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

  • [C11] Matrix Sparsification for Coded Matrix Multiplication
    G. Suh, K. Lee, and C. Suh
    Allerton Conference on Communication, Control and Computing 2017

  • [C12] 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

  • [C13] Large-scale and Interpretable Collaborative Filtering for Educational Data
    K. Lee, J. Chung, and C. Suh
    KDD Workshop on Advancing Education with Data 2017

  • [C14] High-Dimensional Coded Matrix Multiplication
    K. Lee, C. Suh, and K. Ramchandran
    IEEE ISIT 2017

  • [C15] Coded Computation for Multicore Setups
    K. Lee, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran
    IEEE ISIT 2017

  • [C16] Information-theoretic Limits of Subspace Clustering
    K. Ahn, K. Lee, and C. Suh
    IEEE ISIT 2017

  • [C17] 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

  • [C18] 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

  • [C19] Community Recovery in Hypergraphs
    K. Ahn, K. Lee, and C. Suh
    Allerton Conference on Communication, Control and Computing 2016

  • [C20] Speeding Up Distributed Machine Learning Using Codes
    K. Lee, M. Lam, R. Pedarsani, and K. Ramchandran
    NIPS Workshop on Machine Learning Systems 2015, IEEE ISIT 2016

  • [C21] SAFFRON: Sparse-Graph Code Framework for Group Testing
    K. Lee, R. Pedarsani, and K. Ramchandran
    IEEE ISIT 2016

  • [C22] On Scheduling Redundant Requests with Cancellation Overheads
    K. Lee, R. Pedarsani, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2015

  • [C23] Sparse Covariance Estimation Based on Sparse-Graph Codes
    R. Pedarsani, K. Lee, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2015

  • [C24] Fast and Robust Compressive Phase Retrieval with Sparse- Graph Codes
    D. Yin, K. Lee, and K. Ramchandran
    IEEE ISIT 2015

  • [C25] Capacity-Approaching PhaseCode for Low-Complexity Compressive Phase Retrieval
    R. Pedarsani, K. Lee, and K. Ramchandran
    IEEE ISIT 2015

  • [C26] 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

  • [C27] The MDS Queue: Analysing the Latency Performance of Codes
    N. Shah, K. Lee, and K. Ramchandran
    IEEE ISIT 2014

  • [C28] When Do Redundant Requests Reduce Latency?
    N. Shah, K. Lee, and K. Ramchandran
    Allerton Conference on Communication, Control and Computing 2013

  • [C29] 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)

  • [C30] 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

  • [C31] 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

  • [C32] Experiment evaluation of optimal CSMA
    B. Nardelli, J. Lee, K. Lee, Y. Yi, S. Chong, E. Knightly, and M. Chiang
    IEEE INFOCOM 2011

Invited Talks

  • Speeding Up Distributed Machine Learning Using Codes
    National Information Society Agency, Daegu, Korea, Januaray 2018
    Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea, Januaray 2018
    Institute of New Media and Communications, Seoul National University, Seoul, Korea, December 2017
    Naver, Seongnam, Korea, May 2017
    Information Theory and Machine Learning Workshop, KAIST, Daejeon, Korea, May 2017
    Samsung Electronics DMC R&D Center, Seoul, Korea, June 2016
    Information Theory and Applications Workshop, La Jolla, CA, February 2016

  • A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation
    IEEE Communication Theory Workshop, Dana Point, CA, May 2015
    University of Seoul, Seoul, Korea, May 2015

  • The MDS Queue: Analysing the Latency Performance of Codes
    Laboratory of Network Architecture Design and Analysis, KAIST, Daejeon, Korea, May 2014
    IEEE International Conference on Big Data, Santa Clara, CA, October 2013

  • When Do Redundant Requests Reduce Latency?
    DIMACS Workshop on Algorithms for Green Data Storage, Rutgers University, NJ, December 2013

  • Binary Rating Estimation with Graph Side Information
    UC Berkeley BASiCS Seminar, Berkeley, CA, November 2017

  • Sub-linear Time Algorithms for Sparse Signal Recovery Based on Sparse-graph Codes
    Institute of New Media and Communications, Seoul National University, Seoul, Korea, January 2016

  • Introduction to Machine Learning
    National Information Society Agency, Daegu, Korea, November 2016