Twenty papers got accepted to NeurIPS 2020!

Twenty papers from the KAIST Graduate School of AI (“KAIST AI”) got accepted to the Thirty-fourth Annual Conference on Neural Information Processing Systems (NeurIPS 2020).
NeurIPS is one of the top conferences in Machine Learning. In 2020, it will take place virtually from Dec 6 to Dec 12.

List of papers:

1. Minimal Regret in Online Recommendation Systems
Kaito Ariu, Narae Ryu (KAIST AI), Se-Young Yun  (KAIST AI) and Alexandre Proutiere

2. Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park and Jinwoo Shin (KAIST AI)

3. Time-Reversal Symmetric ODE Network
In Huh, Eunho Yang, Sung Ju Hwang and Jinwoo Shin (KAIST AI)

4. CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack*  (KAIST AI), Sangwoo Mo*, Jongheon Jeong and Jinwoo Shin (KAIST AI)

5. Guiding Deep Molecular Optimization with Genetic Exploration
Sungsoo Ahn, Junsu Kim (KAIST AI), Hankook Lee and Jinwoo Shin (KAIST AI)

6. Consistency Regularization for Certified Robustness of Smoothed Classifiers
Jongheon Jeong and Jinwoo Shin (KAIST AI)

7. Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Younggyo Seo*  (KAIST AI), Kimin Lee*, Ignasi Clavera, Thanard Kurutach, Jinwoo Shin (KAIST AI) and Pieter Abbeel

8. Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning
Youngsung Kim, Jinwoo Shin (KAIST AI), Eunho Yang (KAIST AI)  and Sung Ju Hwang (KAIST AI)

9. Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang (KAIST AI), Sung Ju Hwang (KAIST AI) and Jinwoo Shin (KAIST AI)

10. Learning from Failure: De-biasing Classifier from Biased Classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee and Jinwoo Shin (KAIST AI)

11. Reinforcement Learning for Control with Multiple Frequencies
Jongmin Lee, Byung-Jun Lee and Kee-Eung Kim (KAIST AI)

12. Variational Interaction Information Maximization for Cross-domain Disentanglement
HyeongJoo Hwang (KAIST AI), Geon-Hyeong Kim, Seunghoon Hong and Kee-Eung Kim (KAIST AI)

13. Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction
Jinheon Baek (KAIST AI), Dong Bok Lee (KAIST AI) and Sung Ju Hwang (KAIST AI)

14. Adversarial Self-Supervised Contrastive Learning
Minseon Kim (KAIST AI), Jihoon Tack (KAIST AI) and Sung Ju Hwang (KAIST AI)

15. MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures
Jeong Un Ryu, JaeWoong Shin (KAIST AI), Hae Beom Lee (KAIST AI) and Sung Ju Hwang (KAIST AI)

16. Bootstrapping Neural Processes
Juho Lee (KAIST AI), Yoonho Lee, Jungtaek Kim, Eunho Yang (KAIST AI), Sung Ju Hwang (KAIST AI) and Yee Whye Teh

17. Neural Complexity Measures
Yoonho Lee, Juho Lee (KAIST AI), Sung Ju Hwang (KAIST AI), Eunho Yang (KAIST AI) and Seungjin Choi

18. Attribution Preservation in Network Compression for Reliable Network Interpretation
Geondo Park (KAIST AI), June Yong Yang (KAIST AI), Sung Ju Hwang (KAIST AI) and Eunho Yang (KAIST AI)

19. A fair classifier using kernel density estimation’
Jaewoong Cho, Gyeongjo Hwang and Changho Suh (KAIST AI)

20. Binary matrix completion with hierarchical graph side information
Adel Elmahdy, Junhyung Ahn, Changho Suh (KAIST AI) and Soheil Mohaje