An interesting reading

I came accross this interesing paper by a statistician Leo Breiman talking about the difference of statistics and machine learning. Basically, there are two school: one takes the data model, trying to understand the nature process (which is widely studied by statistician), the other one  takes an algorithmic model. They do not care the process in the black box, but just try to approximate the process using whatever effective methods such as neural network and decisions trees (this is the philosophy of machine learning guys).


Some of the discusssions are very incisive. Leo argued that the over emphasize of data model might lead to wrong conclusions. As there could be many different but comparable models to achieve the same performance. Taking a machine learning approach seems more practical.



I am more interested in reading a paper by a machine learning guy talking about statistics. 


Especially, for a newbie to work on machine learning, should he go to the statistics department or computer science department?

refeshing by writing papers

I'm not sure whether this is a good symptom for reseachers. I feel excited when I write papers. This has been confirmed many times.

starting job search

After submitting all the journal papers relevant to my past work, I'm now ready to look for jobs.

Go Go Go!

Hope I can find an ideal job :)

US visa still pending...

As I am planning to go to HK to attend CIKM'09, I went to Mexico to renew my US visa two weeks ago.  As expected, I got checked. But fortunatelly, I "slipped" back into US.

Until today, I still didn't get any update from the consulate about the visa.

Hope I will receive the good news next week!

determine number of clusters?

At present, many community detection methods have been proposed. "Unfortunately", most of these methods require users to specify the number of clusters, which seems insane at first glimpse.

Imaging a conversation like this:

Person: powerful machine, can you tell me something about the data set I have?
Machine: You honored! Sure, but please tell me how many clusters are there.
Person: I do not know the data set. That's why I am asking you!
Machine: Sorry, I cannot tell you anything unless you tell me something.
Person: ....... (stupid machine!)
Machine: ...... (this user is an idiot about data mining!)

So when an algorithm claims that it automatically determines the number of clusters, everyone just embraces it. For instance, modularity is claimed to find the optimal number of clusters. So are Bayesian guys by enforcing a Chinese restaurant process as a prior. These methods, as I understand, is like setting a threshold for the clustering process or setting an objective to optimize. It basically tells a user that under this objective function, this number of clusters seems reasonable. Instead of setting the number of clusters, these algorithm implicitly set a parameter or an biased objective function to optimize.

But do we really need to determine the number of clusters?

If just for data exploration, I think a hierarchical organization of data objects are more reasonable. But it seems that current state of hierarchical clustering is too far away from satisfactory. Most methods just give you a binary tree, which does not reflect any interesting structure. Another disadvantage is high sensitivity of the hierarchical structure to the algorithm implementation and data processing order.

Instead of begging for machines to determine the number of communities, organization clusters of multi-resolutions is more interesting and more consistent with users' request.

IJCAI post-conference report

This year, thanks to the support from IJCAI conference, I can travel to Pasadena to attend the IJCAI conference. This is the first time I attended IJCAI (I attended AAAI in 2005). AI motivated me to pursue my Ph.D study in the first place. I was quite excited to attend this top conference on AI.

Overall, I like the invited talks and demos most. AI is now so separated into different subfields and each field currently (e.g., machine learning and data mining) rarely talks much about intelligence. So these high-quality invited talks in IJCAI really gave me a fresh view of AI from different perspectives. However, the papers in the conference, I have to say, are not quite interesting. What is even worse, it is difficult to grasp any idea if you are not working directly in the field the paper is talking about. While it seems IJCAI motivates to encourage collaboration and cross-discipline talks between AI researchers, the barrier is heavier than ever before. This makes me worry about the quality of the IJCAI conference. How to attract high-quality work to publish in IJCAI and break the boundary between different areas require a lot of efforts, or even a revolutionary reform. Maybe, it is more reasonable to host IJCAI as a symposium consists of high-quality talks and pioneer work, rather than yet-another conference to publish delta papers.

This conference provides two tours for APL and USC. I think these tours were great and such kind of activities should be kept in the following conferences as they provide a great opportunity to explore classical AI projects. It was a little sad that some attendants reserved the seats but did not show up. On the contrary, some other people who wish to join the tour could not go because they had no ticket. I hope the ticket-exchange at the registration desk would be allowed to accommodate more people in the next IJCAI.

Another is concerning the organization of IJCAI. The sessions were well organized. But the catering was really poor. I attended the student reception. The food there was not attractive at all. Some other attendants were also complaining about the food. Maybe, the conference organizer spent most of the fundings to provide travel scholarship for students. Then, I'll take my words back given such a bad economic situation.

I would like to thank IJCAI to provide me this great opportunity to make friends with other AI researchers, to meet those AI “stars”, and to re-cherish those AI dreams I used to have. Hope this conference would become better and inspire more AI communications and explorations.

what's your chance of acceptance if you submit late?

Just came across this question:

The same paper, which one would have a higher chance of being accepted in a conference?

Submit early? normal? or submit late?


Basically, would a larger submission number gives you a higher chance of being accepted?

I believe so.

Actually, as a reviewer and author, I have seen many people rushing their paper for a conference especially in CS. So a large submission number automatically hint that "the paper is rushed". So somehow the reviewer would have a low expectation. Then a good quality paper would stands out in the local region and is likely to get accepted.

Of course, this is just a small trick and no evidence can be found.

A simple way to avoid this bias is to reshuffle the paper when assigning them to the reviwers. Such that the paper's reviewer number has no correlation with the quality. Then, that seems a more fair process.

Just my 2 cents.