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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)

News

New preprints

New publications

  • (May 2022) One paper is accepted to ICML 2022.
    • GenLabel: Mixup Relabeling using Generative Models
  • (Apr. 2022) One paper is accepted to ISIT 2022
    • Breaking Fair Binary Classification with Optimal Flipping Attacks
  • (Apr. 2022) One paper is published to GetMobile: Mobile Computing and Communications
    • The Roaming Edge and its Applications
  • (Mar. 2022) One paper is accepted to ACL 2022 Workshop LT-EDI
    • Debiasing Pre-Trained Language Models via Efficient Fine-tuning
  • (Jan. 2022) One paper is accepted to ICLR 2022.
    • Permutation-Based SGD: Is Random Optimal?
  • (Jan. 2022) Two papers are accepted to FL-AAAI-22.
    • Federated Unsupervised Clustering with Generative Models
    • Improving Fairness via Federated Learning
  • (Sep. 2021) Two papers are accepted to NeurIPS 2021.
    • Sample Selection for Fair and Robust Training
    • Gradient Inversion with Generative Image Prior
  • (June 2021) Two papers are accepted to ICML 2021 Workshops.
    • Gradient Inversion with Generative Image Prior
    • Empirical Study on the Effective VC Dimension of Low-rank Neural Networks
  • (May 2021) Two papers are accepted to ICML 2021.
    • Coded-InvNet for Resilient Prediction Serving Systems long oral
    • Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
  • (Jan. 2021) One paper is accepted to MLSys 2021.
    • Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification.
  • (Jan. 2021) One paper is accepted to ICLR 2021.
    • FairBatch: Batch Selection for Model Fairness

Invited talk

  • 2022
    • (Apr) USC EE, “On a bilevel optimization approach to fair classification”
    • (Apr) UC Santa Barbara CCDC Seminar Series, “On a bilevel optimization approach to fair classification”
    • (Mar) KAIST EE Colloquium, “On Trustworthy and Scalable Machine Learning”
    • (Mar) Informs Optimization Soceity conference 2022, “On a bilevel optimization approach to fair classification”
    • (Feb) UC Berkekley BLISS Seminar Series, “Improving Fairness via Federated Learning”
  • 2011
    • (Dec) KAIST AI International Symposium, “Improving Fairness via Federated Learning”
    • (Oct) KRAFTON Developer Connect, “On Trustworthy Machine Learning”
    • (Sep) Visiting Professor Series, American Family Insurance.
    • (Jun) AI institute of POSTECH, “Information Theory and Coding for Trustworthy and Scalable Machine Learning”
    • (Jun) The Shannon meets Turing Colloquium @ Seoul National University, “Information Theory and Coding for Trustworthy and Scalable Machine Learning”
    • (Jun) KRAFTON, Inc. and Furiosa.ai, “Recent Trends of AI Research”
    • (Apr) IFDS Ethics & Algorithms SIG, “Fairness in AI”
    • (Feb) Invited lecture on Fairness in AI as a part of Lectures on Machine Learning based ICT, Korea Information and Communications Society