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.