Kangwook Lee
Assistant Professor
Department of Electrical and Computer Engineering
Department of Computer Sciences (by courtesy)
University of Wisconsin-Madison
Affiliations:
MLOPT Research Group
Institute for Foundations of Data Science (IFDS)
Email: +first_name.last_name+ at +wisc dot edu
Office: M1002G, Engineering Centers Building
Phone: +1-608-265-4841
Webpage, (by publication date) (by citation)
New work
- (Jan. 2022) Our new work on GAN conditioning is available on Arxiv. Improved Input Reprogramming for GAN Conditioning.
- (Jan. 2022) Our new work on Mixup relabeling is available on Arxiv. GenLabel: Mixup Relabeling using Generative Models.
- (Jan. 2022) AAAI 2022 Workshop Our new works on fair learning and clustering for federated learning are accepted to FL-AAAI-22. Congratulations!
- J. Chung, K. Lee, and K. Ramchandran
Federated Unsupervised Clustering with Generative Models - Y. Zeng, H. Chen, and K. Lee
Improving Fairness via Federated Learning
- J. Chung, K. Lee, and K. Ramchandran
- (Oct. 2021) Our new work on federated fair learning is available on Arxiv. Improving Fairness via Federated Learning.
- (Sep. 2021) NeurIPS 2021 Two papers are accepted to NeurIPS 2021. Congratulations!
- Y. Roh, K. Lee, S. Whang, and C. Suh
Sample Selection for Fair and Robust Training - J. Kim, J. Jeon, K. Lee, S. Oh, and J. Ok
Gradient Inversion with Generative Image Prior
- Y. Roh, K. Lee, S. Whang, and C. Suh
- (June 2021) ICML 2021 Workshops Two papers are accepted to ICML 2021 Workshops.
- J. Jeon, J. Kim, K. Lee, S. Oh, and J. Ok
Gradient Inversion with Generative Image Prior - D. Seo, H. Wang, D. Papailiopoulos, and K. Lee
Empirical Study on the Effective VC Dimension of Low-rank Neural Networks
- J. Jeon, J. Kim, K. Lee, S. Oh, and J. Ok
- (May 2021) ICML 2021 Two papers are accepted to ICML 2021. Congratulations, Tuan and Changhun!
- T. Dinh and K. Lee
Coded-InvNet for Resilient Prediction Serving Systems long oral - C. Jo and K. Lee
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
- T. Dinh and K. Lee
- (Feb. 2021) Our new work on permutation-based SGD is available on Arxiv. Permutation-Based SGD: Is Random Optimal?.
- (Jan. 2021) MLSys 2021 Our work on adaptive gradient communication for distributed ML is accepted.
- S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos.
Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.
- S. Agarwal, H. Wang, K. Lee, S. Venkataraman, and D. Papailiopoulos.
- (Jan. 2021) ICLR 2021 Our work on adaptive sampling for group fairness is accepted to ICLR 2021.
- Y. Roh, K. Lee, S. Whang, and C. Suh
FairBatch: Batch Selection for Model Fairness
- Y. Roh, K. Lee, S. Whang, and C. Suh
Award, Grant & Gift
- (June 2021) Krafton, Inc. has given us generous gift to support our work on Trustworthy and Scalable ML Algorithms and Systems! Thanks a lot, and we are looking forward to more collaborations!
- (Apr. 2021) Our project on AI fairness and its societal impacts is chosen as a recipient of Understanding and Reducing Inequalities Initiative!
Members
- (Sep. 2021) Joseph Shenouda joined the lab! Welcome!
- (Aug. 2021) Ruisu Zhang and Andrew Geng joined the lab! Welcome!
- (Aug. 2021) Dr. Daewon Seo joined DGIST as an Assistant Professor! Congrats!
- (June 2021) Yuchen Zeng and Ziqian Lin joined the lab! Welcome!
- (Feb. 2021) Jy-yong Sohn (co-hosted with Prof. Dimitris Papailiopoulos) came back to Madison as a postdoc! Welcome!
- (Feb. 2021) Liang Shang, Ethan Grover, and Michael Gira joined the lab. Welcome!
Invited talk
- (Feb. 2022) Will give an invited talk at BLISS, University of California, Berkeley. “Improving Fairness via Federated Learning”
- (Dec. 2021) Gave an invited talk at KAIST AI International Symposium. “Improving Fairness via Federated Learning”
- (Oct. 2021) Gave an invited talk at KRAFTON Developer Connect. “On Trustworthy Machine Learning”
- (Sep. 2021) Gave an invited talk at the Visiting Professor Series @ American Family Insurance.
- (June 2021) Gave an invited talk @ AI institute of POSTECH. “Information Theory and Coding for Trustworthy and Scalable Machine Learning”
- (June 2021) Gave an invited talk at the Shannon meets Turing Colloquium @ Seoul National University. “Information Theory and Coding for Trustworthy and Scalable Machine Learning”
- (June 2021) Gave invited talks at KRAFTON, Inc. and Furiosa.ai. “Recent Trends of AI Research”
- (Apr. 2021) Gave an invited talk on “Fairness in AI” at IFDS Ethics & Algorithms SIG
- (Feb. 2021) Gave an invited lecture on Fairness in AI as a part of Lectures on Machine Learning based ICT, organized by Korea Information and Communications Society