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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
  • (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
  • (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
  • (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) 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

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