ICML 2012 马上就要开了，这是录取的论文列表：accepted papers。
Machine Learning 各个研究分支投稿录取比率如何，各个方向投稿多少如何？
博客Machine Learning（Theory）上做了一个统计，感兴趣的可以去看看。
| 18/66 = 0.27 | in (0.18,0.36) | Reinforcement Learning |
| 10/52 = 0.19 | in (0.17,0.37) | Supervised Learning |
| 9/51 = 0.18 | not in (0.18, 0.37) | Clustering |
| 12/46 = 0.26 | in (0.17, 0.37) | Kernel Methods |
| 11/40 = 0.28 | in (0.15, 0.4) | Optimization Algorithms |
| 8/33 = 0.24 | in (0.15, 0.39) | Learning Theory |
| 14/33 = 0.42 | not in (0.15, 0.39) | Graphical Models |
| 10/32 = 0.31 | in (0.15, 0.41) | Applications (+5 invited) |
| 8/29 = 0.28 | in (0.14, 0.41]) | Probabilistic Models |
| 13/29 = 0.45 | not in (0.14, 0.41) | NN & Deep Learning |
| 8/26 = 0.31 | in (0.12, 0.42) | Transfer and Multi-Task Learning |
| 13/25 = 0.52 | not in (0.12, 0.44) | Online Learning |
| 5/25 = 0.20 | in (0.12, 0.44) | Active Learning |
| 6/22 = 0.27 | in (0.14, 0.41) | Semi-Supervised Learning |
| 7/20 = 0.35 | in (0.1, 0.45) | Statistical Methods |
| 4/20 = 0.20 | in (0.1, 0.45) | Sparsity and Compressed Sensing |
| 1/19 = 0.05 | not in (0.11, 0.42) | Ensemble Methods |
| 5/18 = 0.28 | in (0.11, 0.44) | Structured Output Prediction |
| 4/18 = 0.22 | in (0.11, 0.44) | Recommendation and Matrix Factorization |
| 7/18 = 0.39 | in (0.11, 0.44) | Latent-Variable Models and Topic Models |
| 1/17 = 0.06 | not in (0.12, 0.47) | Graph-Based Learning Methods |
| 5/16 = 0.31 | in (0.13, 0.44) | Nonparametric Bayesian Inference |
| 3/15 = 0.20 | in (0.7, 0.47) | Unsupervised Learning and Outlier Detection |
| 7/12 = 0.58 | not in (0.08, 0.50) | Gaussian Processes |
| 5/11 = 0.45 | not in (0.09, 0.45) | Ranking and Preference Learning |
| 2/11 = 0.18 | in (0.09, 0.45) | Large-Scale Learning |
| 0/9 = 0.00 | in [0, 0.56) | Vision |
| 3/9 = 0.33 | in [0, 0.56) | Social Network Analysis |
| 0/9 = 0.00 | in [0, 0.56) | Multi-agent & Cooperative Learning |
| 2/9 = 0.22 | in [0, 0.56) | Manifold Learning |
| 4/8 = 0.50 | not in [0, 0.5) | Time-Series Analysis |
| 2/8 = 0.25 | in [0, 0.5] | Large-Margin Methods |
| 2/8 = 0.25 | in [0, 0.5] | Cost Sensitive Learning |
| 2/7 = 0.29 | in [0, 0.57) | Recommender Systems |
| 3/7 = 0.43 | in [0, 0.57) | Privacy, Anonymity, and Security |
| 0/7 = 0.00 | in [0, 0.57) | Neural Networks |
| 0/7 = 0.00 | in [0, 0.57) | Empirical Insights |
| 0/7 = 0.00 | in [0, 0.57) | Bioinformatics |
| 1/6 = 0.17 | in [0, 0.5) | Information Retrieval |
| 2/6 = 0.33 | in [0, 0.5) | Evaluation Methodology |
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