ICML-10 Submission Statistics by Area

 

primary subject area

   

primary and secondary

   

Submission Keyword

acc

rej

sub

%

acc

rej

sub

%

Time-Series Analysis

5

3

8

62.50

9

15

24

37.50

Deep Architectures

6

4

10

60.00

10

7

17

58.82

Multi-Agent and Co-Operative Learning

4

3

7

57.14

5

8

13

38.46

Cognitive Models of Learning

2

2

4

50.00

2

10

12

16.67

Planning and Control

1

1

2

50.00

9

16

25

36.00

Graph Mining

1

1

2

50.00

2

8

10

20.00

Bayesian Inference

7

9

16

43.75

20

44

64

31.25

Learning Theory

6

9

15

40.00

15

34

49

30.61

Cost-Sensitive Learning

2

3

5

40.00

2

9

11

18.18

Optimization Algorithms

9

16

25

36.00

24

67

91

26.37

Latent-Variable Models and Topic Models

5

9

14

35.71

11

24

35

31.43

Gaussian Processes

3

6

9

33.33

6

19

25

24.00

Ensemble Methods

2

4

6

33.33

6

14

20

30.00

Monte Carlo Methods

1

2

3

33.33

8

16

24

33.33

Recommender Systems

1

2

3

33.33

3

8

11

27.27

Outlier Detection

1

2

3

33.33

2

9

11

18.18

Reinforcement Learning

16

33

49

32.65

21

44

65

32.31

Clustering

8

18

26

30.77

15

51

66

22.73

Online Learning

6

14

20

30.00

13

41

54

24.07

Large-Margin Methods

3

7

10

30.00

13

35

48

27.08

Feature Selection and Dimensionality Reduction

9

22

31

29.03

17

52

69

24.64

Semi-Supervised Learning

6

16

22

27.27

13

43

56

23.21

Matrix Factorization Methods

3

8

11

27.27

8

18

26

30.77

Structured Output Prediction

2

6

8

25.00

5

18

23

21.74

Active Learning

4

13

17

23.53

7

21

28

25.00

Kernel Methods

6

20

26

23.08

21

61

82

25.61

Probabilistic Models

6

22

28

21.43

31

77

108

28.70

Statistical Methods

3

11

14

21.43

15

53

68

22.06

Manifold Learning

3

11

14

21.43

4

23

27

14.81

Ranking and Preference Learning

3

12

15

20.00

7

19

26

26.92

Sparsity and Compressed Sensing

2

8

10

20.00

15

33

48

31.25

Large-Scale Learning

2

8

10

20.00

16

27

43

37.21

Evaluation Methodology and ROC Analysis

1

4

5

20.00

1

7

8

12.50

Partially Observable Markov Decision Processes

1

4

5

20.00

2

6

8

25.00

Vision

1

5

6

16.67

9

34

43

20.93

Transfer and Multi-Task Learning

3

17

20

15.00

9

31

40

22.50

Information Retrieval

1

6

7

14.29

3

26

29

10.34

Supervised Learning

3

20

23

13.04

21

96

117

17.95

Neural Networks

1

7

8

12.50

10

26

36

27.78

Graph-Based Learning Methods

1

7

8

12.50

9

24

33

27.27

Statistical Relational Learning

1

8

9

11.11

4

19

23

17.39

Text Mining

1

9

10

10.00

2

18

20

10.00

Unsupervised Learning

0

13

13

0.00

18

56

74

24.32

Empirical Insights into ML

0

5

5

0.00

2

26

28

7.14

None of the above

0

5

5

0.00

0

5

5

0.00

Bioinformatics

0

3

3

0.00

6

14

20

30.00

Inductive Logic Programming and Relational Learning

0

3

3

0.00

1

8

9

11.11

Social Network Analysis

0

3

3

0.00

1

6

7

14.29

Robotics

0

2

2

0.00

2

14

16

12.50

Hidden Markov Models

0

2

2

0.00

2

8

10

20.00

Data Streams

0

2

2

0.00

1

8

9

11.11

Knowledge-Intensive Learning

0

2

2

0.00

0

4

4

0.00

Fuzzy Learning Systems

0

2

2

0.00

0

3

3

0.00

Natural Language Processing

0

1

1

0.00

5

13

18

27.78

Rule and Decision Tree Learning

0

1

1

0.00

2

8

10

20.00

Game Theory

0

1

1

0.00

3

5

8

37.50

Visualization

0

1

1

0.00

1

6

7

14.29

Game Playing

0

1

1

0.00

2

4

6

33.33

Pattern Mining and Inductive Querying

0

1

1

0.00

1

4

5

20.00

Meta-Learning

0

1

1

0.00

1

4

5

20.00

Discovery

0

0

0

1

5

6

16.67

Evolutionary Computation

0

0

0

3

2

5

60.00

Web Mining

0

0

0

0

3

3

0.00

Case-Based Reasoning

0

0

0

2

0

2

100.00

Constructive Induction and Theory Revision

0

0

0

1

0

1

100.00