### Research Overview

I develop trustworthy and scalable ML algorithms and systems while making the most efficient use of data.

Q1. How can we design trustworthy and scalable ML systems?

Q2. How can we design efficient ML algorithms?

Q3. How much data is needed for reliable and efficient ML, and what should I do if I don’t have enough data?

Q4. How can we solve real-world problems using ML?

Some of my recent work can be roughly clustered as follows.

#### Q1. How can we design trustworthy and scalable ML systems?

- Breaking Fair Binary Classification with Optimal Flipping Attacks

C. Jo, J. Sohn, and K. Lee

Arxiv 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 2022 Workshop on Federated Learning - Improving Fairness via Federated Learning

Y. Zeng, H. Chen, and K. Lee

AAAI 2022 Workshop on Federated Learning - 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 - Coded-InvNet for Resilient Prediction Serving Systems

T. Dinh, and K. Lee

ICML 2021 long oral - 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 - 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 - FR-Train: A mutual information-based approach to fair and robust training

Y. Roh, K. Lee, S. Whang, and C. Suh

ICML 2020 - Improving Model Robustness via Automatically Incorporating Self-supervision Tasks

D. Kim, K. Lee, and C. Suh

NeurIPS 2019 Workshop on Meta-Learning - 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 - 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 - High-Dimensional Coded Matrix Multiplication

K. Lee, C. Suh, and K. Ramchandran

IEEE ISIT 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 - A VoD System for Massively Scaled, Heterogeneous Environments: Design and Implementation

K. Lee, L. Yan, A. Parekh, and K. Ramchandran

IEEE MASCOTS 2013

#### Q2. How can we design efficient ML algorithms?

- Rare Gems: Finding Lottery Tickets at Initialization

K. Sreenivasan, J. Sohn, L. Yang, M. Grinde, A. Nagle, H. Wang, K. Lee, D. Papailiopoulos

Arxiv 2022 - Permutation-Based SGD: Is Random Optimal?

S. Rajput, K. Lee, and D. Papailiopoulos

ICLR 2022 - GenLabel: Mixup Relabeling using Generative Models

J. Sohn, L. Shang, H. Chen, J. Moon, D. Papailiopoulos, and K. Lee

ICML 2022

#### Q3. How much data is needed for reliable and efficient ML, and what should I do if I don’t have enough data?

- Improved Input Reprogramming for GAN Conditioning

T. Dinh, D. Seo, Z. Du, L. Shang, and K. Lee

Arxiv 2022 - Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

C. Jo, and K. Lee

ICML 2021 - Reprogramming GANs via Input Noise Design

K. Lee, C. Suh, and K. Ramchandran

ECML PKDD 2020 - SAFFRON: Sparse-Graph Code Framework for Group Testing

K. Lee, 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 - Binary Rating Estimation with Graph Side Information

K. Ahn, K. Lee, H. Cha, and C. Suh

NeruIPS 2018 - 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 - Simulated+Unsupervised Learning With Adaptive Data Generation and Bidirectional Mappings

K. Lee*, H. Kim*, and C. Suh

ICLR 2018 - Information-theoretic Limits of Subspace Clustering

K. Ahn, K. Lee, and C. Suh

IEEE ISIT 2017 - 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

#### Q4. How can we solve real-world problems using ML?

- Crash to Not Crash: Learn to Identify Dangerous Vehicles using a Simulator

H. Kim, K. Lee, G. Hwang, and C. Suh

AAAI 2019, 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