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 much data is needed for reliable ML, and what should I do if I don't have enough data?
Q3. How can we solve realworld problems using ML?
Some of my recent work can be roughly clustered as follows.
Q1. How can we design trustworthy and scalable ML systems?
CodedInvNet 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
FRTrain: A mutual informationbased approach to fair and robust training
Y. Roh, K. Lee, S. Whang, and C. Suh
ICML 2020
Improving Model Robustness via Automatically Incorporating Selfsupervision Tasks
D. Kim, K. Lee, and C. Suh
NeurIPS 2019 Workshop on MetaLearning (MetaLearn 2019)
UberShuffle: Communicationefficient 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
HighDimensional 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 much data is needed for reliable ML? How can we design efficient algorithms? What should I do if I don't have enough data?
DiscreteValued 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: SparseGraph 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
Informationtheoretic Limits of Subspace Clustering
K. Ahn, K. Lee, and C. Suh
IEEE ISIT 2017
PhaseCode: Fast and Efficient Compressive Phase Retrieval based on SparseGraphCodes
R. Pedarsani, D. Yin, K. Lee, and K. Ramchandran
IEEE Transactions on Information Theory June 2017
Q3. How can we solve realworld 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
Largescale 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
