임성빈(林聖彬)
Lim, Sungbin

Research Area & Collaboration

My recent research focuses on a Computational Statistics approach to incomplete data problems in machine learning (including deep learning) from the data-driven and trustworthy AI perspective. I am recently interested in the following topics:

• • Probabilistic Machine Learning: 

    • Generative Models (NeurIPS '23, NeurIPS '23, NeurIPS '24)

    • Robust Learning (ICRA '18, NeurIPS '21, EACL '23, CVPR '20)

  • Stochastic Optimization

    • Bandit (NeurIPS '20 , TNNLS '22) & Black-box Optimization (AAAI '20)

    • Reinforcement Learning (RSS '20, TR Part C '23)

    • AutoML (NeurIPS '19, MICCAI '19)

  • Causal Learning & Machine Reasoning

    • Counterfactual Generation (ACML w)

    • Data-driven Causal Discovery (NeurIPS '24  CaLM Workshop)

  • AI for Scientific Computing and Discovery

    • Collaboration with LG AI Research

(Important Notice)

현재 고려대학교 인공지능학과 김성웅 교수님과 Artificial General Intelligence Lab 을 공동지도하고 있습니다. 통계학과에서는 기계학습 이론 연구를 희망하는 박사과정 학생 중 수학, 통계학, 최적화, 알고리즘, 기계학습 및 프로그래밍 적성이 매우 우수한 지원자 중 제한적으로 연구실에서 선발하여 연구 지도를 합니다. 연구실 소속 없이 지도교수를 희망하는 통계학과 대학원생 분들은 KUBIG 학회원에 한하여 선발 및 지도하고 있으니 참고 바랍니다.

프로그래밍(C++, Python, Julia), 기계학습 모델링, 고성능 컴퓨팅에 숙달한 소프트웨어 개발자나 박사후 연구원은 기업 협업을 통해 공동연구 프로젝트에 참여할 수 있는 기회를 제공하고 있습니다. 

Office Hour & Consulting Service

고려대학교 재학 중인 학부생 분들께 AMA(Ask Me Anything) 상담을 제공하고 있습니다. 링크를 통해 신청하시면 됩니다 [Link].

Selected Publications

See [Google Scholar] for other publications.

• (new) On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery, NeurIPS Workshop on Causality and Large Models, 2024 [paper]

• (new) Stochastic Optimal Control for Diffusion Bridges in Function Spaces, NeurIPS, 2024 [paper]

• (new) Slice and Conquer: A Planar-to-3D Framework for Efficient Interactive Segmentation of Volumetric Images, WACV, 2024 [paper]

• Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces, NeurIPS (Spotlight), 2023 [paper]

• Score-based Generative Models with Lévy Processes, NeurIPS (Spotlight), 2023 [paper]

• Bag of Tricks for In-Distribution Calibration of Pretrained Transformers, EACL, 2023 [paper]

• • A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone, TR Part C (IF: 9.022), 2023 [paper]

  • Neural Bootstrapper, NeurIPS, 2021 [paper] [code]

  • Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards, NeurIPS, 2020 [paper]

  • Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Network, CVPR (Oral), 2020 [video] [paper] [code] 

  • Monte Carlo Tree Search in continuous spaces using Voronoi optimistic optimization with regret bounds, AAAI (Oral), 2020 [paper] [appendix] 

  • Fast AutoAugment, NeurIPS, 2019 [paper] [code]

  • Scalable Neural Architecture Search for 3D Medical Image Segmentation, MICCAI, 2019 [paper]

  • A Sobolev space theory for stochastic partial differential equations with time-fractional derivatives, Ann. Probab., 2019 [paper]

  • An Lq(Lp)-theory for the time fractional evolution equations with variable coefficients, Adv. Math., 2017 [paper]

  • Neural Stain-Style Transfer Learning using GAN for Histopathological Images, ACML Workshop on Machine Learning for AI Platforms, 2017 [paper]

News

• • Our lab students ranked 2nd on the Global Leaderboard and 1st on the Student Leaderboard at ML4CO NeurIPS 2021 Competition. [Leaderboard] [News]

Open Source

• • AutoCLINT: The Winning Method in AutoCV Challenge 2019, 2020 [paper] [code]

  • torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models, 2020 [paper] [code]