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