ICML and NeurIPS are the world’s most prestigious machine learning conferences. The quality and impact of institution’s research in machine learning are often measured by the number of papers accepted at these conferences. KAIST EE has been very prolific in this sense. At 2019 ICML alone, KAIST EE researchers have published 9 papers, becoming one of the most productive institutions of the world in machine learning research. These papers can be found below:
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon, Jun Seo, and Jaekyun Moon
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Seungyul Han and Youngchul Sung
Weak Detection of Signal in the Spiked Wigner Model
Hye Won Chung and Ji Oon Lee
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero and Yung Yi
Learning What and Where to Transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, and Jinwoo Shin
Training CNNs with Selective Allocation of Channels
Jongheon Jeong and Jinwoo Shin
Robust Inference via Generative Classifiers for Handling Noisy Labels
Kimin Lee , Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Kimin Lee and Mantas Mazeika
Spectral Approximate Inference
Sejun Park, Eunho Yang, Se-Young Yun, and Jinwoo Shin
