Kangwook Lee
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Assistant Professor Affiliations: |
Email : first_name.last_name at wisc dot edu
Phone : +1-608-265-4841
Webpage : http://kangwooklee.com
Google Scholar : http://scholar.google.com/citations?user=sCEl8r-n5VEC&hl=en
Lab News
New work
(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
(Feb. 2021) Preprint 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.
(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
(Sep. 2020) NeurIPS 2020 Our work on robust federated learning is accepted to NeurIPS 2020.
H. Wang, K. Sreenivasan, S. Rajput, H. Vishwakarma, S. Agarwal, J. Sohn, K. Lee, and D. Papailiopoulos
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
(June 2020) ECML PKDD 2020 Our new work on GAN reprogramming is accepted to ECML PKDD 2020.
K. Lee, C. Suh, and K. Ramchandran
Reprogramming GANs via Input Noise Design
(May 2020) ICML 2020 Our work on trustworthy AI is accepted to ICML 2020.
Y. Roh, K. Lee, S. Whang, and C. Suh
FR-Train: A mutual information-based approach to fair and robust training
Award, Grant & Gift
(Apr. 2021) Our project on AI fairness and its societal impacts is chosen as a recipient of Understanding and Reducing Inequalities Initiative!
(Oct. 2020) Our project on GAN-mixup is chosen as a recipient of American Family Funding Initiative Awards!
(July 2020) Our project on distributed learning for the nomadic edge is chosen as a recipient of NSF/Intel Partnership on Machine Learning for Wireless Networking Systems (MLWiNS)!
(June 2020) Our work on coded computation received the Joint Communications Society/Information Theory Society Paper Award!
K. Lee, M. Lam, R. Pedarsani, D. Papailiopoulos, and K. Ramchandran, “Speeding up Distributed Machine Learning Using Codes”, IEEE Transactions on Information Theory, vol. 64, no. 3, pp. 1514-1529, March 2018(June 2020) Our project on interdisciplinary engineering of quantum information systems is chosen for the UW 2020: WARF Discovery Initiative!
New members
(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!
(July/Aug. 2020) Tuan Dinh, Nayoung Lee, and Liu Yang joined the lab. Nayoung will be co-advised with Prof. Dimitris Papailiopoulos, and Liu will be co-advised with Prof. Rob Nowak and Prof. Dimitris Papailiopoulos. Welcome!
(Jan./Mar. 2020) Dr. Daewon Seo and Zhixu Du joined the lab! Welcome!
(Nov. 2019) Changhun Jo and Hyecheol Jang joined the lab! Welcome!
(Oct. 2019) Jy-yong Sohn (co-hosted with Prof. Dimitris Papailiopoulos) will visit the lab for five months! Welcome!
Invited talk
(Nov./Dec. 2020) Gave invited talks at the BLISS seminar @ UC Berkeley, SILO seminar @ UW Madison, ML Ideas @ Microsoft Research New England. ‘‘Make-or-break issues in fair classification’’
(May 2020) Gave a talk at Air Force Research Laboratory’s Virtual Workshop on Adversarial Robustness of Machine Learning, ‘‘FR-Train: A mutual information-based approach to fair and robust training’’
(Feb. 2020) Gave an invited talk at the Chaos and Complex Systems Seminar @ UW-Madison.
(Oct. 2019) Gave an invited talk at SILO seminar. ‘‘Learning with Scarce Data: The Role of Side Information, Simulators, and GANs’’