Kimin Lee

Starting Spring 2020, I will be a postdoctoral fellow at UC Berkeley, working with Prof. Pieter Abbeel. Before this, I received a PhD at Korea Advanced Institute of Science and Technology (KAIST), where I was supervised by Prof. Jinwoo Shin. During PhD, I also interned and collaborated closely with Prof. Honglak Lee at University of Michigan.

  • Contact: kiminlee at kaist dot ac dot kr // pokaxpoka at gmail dot com

Updates

Publications (C: conference / J: journal / P: preprint)

[C10] Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild [pdf][code]

[C9] Robust Inference via Generative Classifiers for Handling Noisy Labels [pdf][code]

[C8] Using Pre-Training Can Improve Model Robustness and Uncertainty [pdf] [code]

[C7] A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks [pdf] [code]

[C6] Learning to Specialize with Knowledge Distillation for Visual Question Answering [pdf] [code]

[C5] Hierarchical Novelty Detection for Visual Object Recognition [pdf] [code]

[C4] Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples [pdf] [code]

[C3] Confident Multiple Choice Learning [pdf] [code]

[C2] TravelMiner: On the Benefit of Path-based Mobility Prediction

[C1] Just-in-time WLANs: On-demand Interference-managed WLAN Infrastructures [pdf] [slides]

[J1] Dynamic Control for On-demand Interference-managed WLAN Infrastructures

    • Seokhyun Kim, Kimin Lee, Yeonkeun Kim, Jinwoo Shin, Seungwon Shin and Song Chong
    • IEEE/ACM Transactions on Networking (TON), 2019

[P2] A Simple Randomization Technique for Generalization in Deep Reinforcement Learning [pdf]

    • Kimin Lee*, Kibok Lee*, Jinwoo Shin and Honglak Lee
    • NeurIPS Workshop on Deep Reinforcement Learning 2019 as Oral presentation

[P1] Simplified Stochastic Feedforward Neural Networks [pdf]

    • Kimin Lee, Jaehyung Kim, Song Chong and Jinwoo Shin

Education

  • 2015.02-: Doctor of Philosophy (Ph.D.), School of Electrical Engineering (Machine/deep learning), KAIST (adviser: Jinwoo Shin)
  • 2013.02-2015.02: Master of Science (MS), School of Electrical Engineering (Wireless communication networks), KAIST (adviser: Song Chong and Jinwoo Shin)
  • 2009.02-2013.02: Bachelor of Engineering (BS), School of Electrical Engineering, KAIST

Presented Talks

  • "Generative classifier for detecting out-of-distributions and handling noisy labels", Samsung Research, Seoul, Korea, 2019.
  • "Generative classifier for detecting out-of-distributions and handling noisy labels", Samsung DS, Suwon, Korea, 2019.
  • "Toward overcoming out-of-Distribution examples in deep learning", Prof. Pieter Abbeel's Group, Berkeley, CA, USA, 2019.
  • "Toward overcoming out-of-Distribution examples in deep learning", Prof. Dawn Song's Group, Berkeley, CA, USA, 2019.
  • "Robust Inference via Generative Classifiers for Handling Noisy Labels", ICML, Long Beach, USA, 2019.
  • "Generative classifier for detecting abnormal samples and handling noisy labels", Samsung Advanced Institute of Technology, Suwon, Korea, 2019.
  • "Improving the reliability and robustness of deep learning", LG CNS AI, Seoul, Korea, 2019.
  • "A simple unified framework for detecting out-of-distribution samples and adversarial attacks", NeurIPS, Montreal, Canada, 2018.
  • "Generative classifier for obtaining well-calibrated predictive uncertainty and developing a robust inference method", Google DeepMind, Mountain View, CA, USA, 2018.
  • "Training confidence-calibrated classifiers for detecting out-of-distribution samples", Samsung Advanced Institute of Technology, Suwon, Korea, 2018.
  • "Training confidence-calibrated classifiers for detecting out-of-Distribution samples", NAVER Clova AI, Seoul, Korea, 2018.
  • "Confident multiple choice learning", SK telecom, Seoul, Korea, 2017.
  • "Confident multiple choice learning", ICML, Sydney, Australia, 2017.
  • "Just-in-time WLANs: on-demand interference-managed WLAN infrastructures", INFOCOM, San Francisco, USA, 2016.

Academic Activities

  • Conference reviewer
    • NeurIPS : 2018/19
    • ICML: 2019/20
    • ICLR: 2019/20
    • AAAI: 2020
    • UAI: 2019/20
    • IJCAI: 2020
    • ACML: 2018/19
  • Journal reviewer
    • IEEE Transactions on Pattern Analysis and Machine Intelligence
    • Machine Learning Journal
    • IEEE/ACM Transactions on Networking
    • IEEE Transactions on Neural Networks and Learning Systems
    • IEEE Design & Test

Work Experience