The ICML paper deadline has passed. Joelle and I were surprised to see the number of submissions jump from last year by about 50% to around 900 submissions. A tiny portion of these are immediate rejects(*), so this is a much larger set of papers than expected. The number of workshop submissions also doubled compared to last year, so ICML may grow significantly this year, if we can manage to handle the load well. The prospect of making 900 good decisions is fundamentally daunting, and success will rely heavily on the program committee and area chairs at this point.

For those who want to rubberneck a bit more, here’s a breakdown of submissions by primary topic of submitted papers:

66 Reinforcement Learning
52 Supervised Learning
51 Clustering
46 Kernel Methods
40 Optimization Algorithms
39 Feature Selection and Dimensionality Reduction
33 Learning Theory
33 Graphical Models
33 Applications
29 Probabilistic Models
29 NN & Deep Learning
26 Transfer and Multi-Task Learning
25 Online Learning
25 Active Learning
22 Semi-Supervised Learning
20 Statistical Methods
20 Sparsity and Compressed Sensing
19 Ensemble Methods
18 Structured Output Prediction
18 Recommendation and Matrix Factorization
18 Latent-Variable Models and Topic Models
17 Graph-Based Learning Methods
16 Nonparametric Bayesian Inference
15 Unsupervised Learning and Outlier Detection
12 Gaussian Processes
11 Ranking and Preference Learning
11 Large-Scale Learning
9 Vision
9 Social Network Analysis
9 Multi-agent & Cooperative Learning
9 Manifold Learning
8 Time-Series Analysis
8 Large-Margin Methods
8 Cost Sensitive Learning
7 Recommender Systems
7 Privacy, Anonymity, and Security
7 Neural Networks
7 Empirical Insights into ML
7 Bioinformatics
6 Information Retrieval
6 Evaluation Methodology
<5 each Text Mining, Rule and Decision Tree Learning, Graph Mining,
Planning & Control, Monte Carlo Methods, Inductive Logic Programming & Relational Learning,
Causal Inference, Statistical and Relational Learning, NLP, Hidden Markov Models,
Game Theory, Robotics, POMDPs, Geometric Approaches, Game Playing, Data Streams,
Pattern Mining & Inductive Querying, Meta-Learning, Evolutionary Computation

(*) Deadlines are magical, because they galvanize groups of people to concentrated action. But, they have to be real deadlines to achieve this, which leads us to reject late submissions & format failures to keep the deadline real for future ICMLs. This is uncomfortably rough at times.