has died. He lived a full life. I know him personally as a founder of the Center for Computational Learning Systems and the New York Machine Learning Symposium, both of which have sheltered and promoted the advancement of machine learning. I expect much of the New York area machine learning community will miss him, as well as many others around the world.
The Submodularity workshop and Lucca Professorship
Nina points out the Submodularity Workshop March 19-20 next week at Georgia Tech. Many people want to make Submodularity the new Convexity in machine learning, and it certainly seems worth exploring.
Sara Olson also points out a tenured faculty position at IMT Lucca with a deadline of May 15th. Lucca happens to be the ancestral home of 1/4 of my heritage 🙂
COLT/ICML Open Questions and ICML Instructions
Sasha is the open problems chair for both COLT and ICML. Open problems will be presented in a joint session in the evening of the COLT/ICML overlap day. COLT has a history of open sessions, but this is new for ICML. If you have a difficult theoretically definable problem in machine learning, consider submitting it for review, due March 16. You’ll benefit three ways:
- The effort of writing down a precise formulation of what you want often helps you understand the nature of the problem.
- Your problem will be officially published and citable.
- You might have it solved by some very intelligent bored people.
The general idea could easily be applied to any problem which can be crisply stated with an easily verifiable solution, and we may consider expanding this in later years, but for this year all problems need to be of a theoretical variety.
Joelle and I (and Mahdi, and Laurent) finished an initial assignment of Program Committee and Area Chairs to papers. We’ll be updating instructions for the PC and ACs as we field questions. Feel free to comment here on things of plausible general interest, but email us directly with specific concerns.
Key Scientific Challenges and the Franklin Symposium
For graduate students, the Yahoo! Key Scientific Challenges program including in machine learning is on again, due March 9. The application is easy and the $5K award is high quality “no strings attached” funding. Consider submitting.
Those in Washington DC, Philadelphia, and New York, may consider attending the Franklin Institute Symposium April 25 which has several speakers and an award for V. Attendance is free with an RSVP.
ICML+50%
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.