Sixteen papers got accepted to ICLR 2021!

Sixteen papers from the KAIST Graduate School of AI (“KAIST AI”) got accepted to the Ninth International Conference on Learning Representations (ICLR 2021).

ICLR is one of the top conferences in Machine Learning. In 2021, it is to be held virtually on May 4-7.

List of the papers:

1. Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Dong Bok Lee, Dongchan Min, Seanie Lee, and Sung Ju Hwang (Spotlight Presentation)

2. Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee, and Jinwoo Shin (Spotlight Presentation)

3. Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic
Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, and Kee-Eung Kim (Spotlight Presentation)

4. Accurate Learning of Graph Representations with Graph Multiset Pooling
Jinheon Baek, Minki Kang, and Sung Ju Hwang

5. Monte-Carlo Planning and Learning with Language Action Value Estimates
Youngsoo Jang, Seokin Seo, Jongmin Lee, and Kee-Eung Kim

6. Training GANs with Stronger Augmentations via Contrastive Discriminator
Jongheon Jeong and Jinwoo Shin

7. Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang, and Sung Ju Hwang

8. Representation Balancing Offline Model-based Reinforcement Learning
Byung-Jun Lee, Jongmin Lee, and Kee-Eung Kim

9. Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Hayeon Lee, Eunyoung Hyung, and Sung Ju Hwang

10. Layer-adaptive Sparsity for the Magnitude-based Pruning
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin

11. i-Mix: A Strategy for Regularizing Contrastive Representation Learning
Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, and Honglak Lee

12. Contrastive Learning with Adversarial Perturbations for Conditional Text Generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang

13. BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, and Se-Young Yun,

14. Learning to Sample with Local and Global Contexts in Experience Replay Buffer
Youngmin Oh, Kimin Lee, Jinwoo Shin, Eunho Yang, and Sung Ju Hwang

15. FairBatch: Batch Selection for Model Fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh

16. FedMix: Approximation of Mixup under Mean Augmented Federated Learning
Tehrim Yoon, Sumin Shin, Sung Ju Hwang, and Eunho Yang