This repo organizes various top conferences related to AI and data science to provide statistics on the keywords, themes, and author information of papers accepted by each conference.
Reference: Paper Digest
Top 10 authors:
| author | num_papers | University |
|---|---|---|
| Masashi Sugiyama | 11 | University of Tokyo |
| Michael Jordan | 8 | UC Berkeley |
| Michal Valko | 8 | DeepMind & Inria & ENS |
| Dale Schuurmans | 8 | Google Brain & U of Alberta |
| Zhaoran Wang | 7 | Northwestern U |
| Gang Niu | 7 | RIKEN AIP |
| Mihaela van der Schaar | 7 | University of Cambridge |
| Percy Liang | 7 | Stanford |
| Tommi Jaakkola | 7 | MIT |
| Steven Wu | 6 | U of Minnesota |
| topic | num_papers |
|---|---|
| reinforcement learning | 59 |
| graph | 58 |
| GAN | 17 |
| private | 14 |
| unsupervised | 11 |
| uncertainty | 11 |
| multi-task | 8 |
| generative adversarial | 8 |
| GANs | 7 |
| online learning | 7 |
| semi-supervised | 7 |
| Differential Privacy | 6 |
| few-shot | 6 |
| transfer learning | 5 |
| Federated | 5 |
| Federated learning | 5 |
| convolutional neural networks | 4 |
| Q-learning | 4 |
| time series | 4 |
| CNN | 4 |
| generative adversarial | 4 |
| interpretability | 2 |
| Knowledge Distillation | 1 |
| real time | 1 |
| GNN | 1 |
| real-time | 1 |
summary:
- Total num (RT+ADS), Research Track, and Applied Data Science (ADS) track of paper in KDD 2020 is **338 217 121 **
- Total number of submissions: 2035 (the highest in history, over 13% more than the second highest one)
- Research track(long paper): 1279 submissions, 216 accepted, 216 / 1279 = 16.9%
| topic | num_papers |
|---|---|
| graph | 74 |
| convolutional neural networks | 6 |
| unsupervised | 6 |
| generative adversarial | 6 |
| reinforcement learning | 6 |
| time series | 5 |
| semi-supervised | 5 |
| real-time | 5 |
| GAN | 4 |
| GNN | 4 |
| multi-task | 4 |
| GCN | 3 |
| meta-learning | 3 |
| Federated | 2 |
| privacy | 2 |
| private | 1 |
| interpretability | 1 |
| Knowledge Distillation | 1 |
| co-training | 1 |
| CNN | 1 |
| GANs | 1 |
| transfer learning | 1 |



