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Thought this was cool: ICML 录取统计

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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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from 丕子: http://www.zhizhihu.com/html/y2012/3816.html

Written by cwyalpha

06月 6, 2012 at 4:12 pm

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