### 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