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