Machine Learning (Theory)

Dean Foster and Daniel Hsu had a couple observations about reductions to regression that I wanted to share. This will make the most sense for people familiar with error correcting output codes (see the tutorial, page 11). Many people are comfortable using linear regression in a one-against-all style, where you try to predict the probability [...]

Adam Klivans, points out the COLT call for papers. The important points are: Due Feb 13. Montreal, June 18-21. This year, there is author feedback.

Michael Littman and Leon Bottou have decided to use a franchise program chair approach to reviewing at ICML this year. I’ll be one of the area chairs, so I wanted to mention a few things if you are thinking about naming me. I take reviewing seriously. That means papers to be reviewed are read, [...]

A while ago, we discussed the health of COLT. COLT 2008 substantially addressed my concerns. The papers were diverse and several were interesting. Attendance was up, which is particularly notable in Europe. In my opinion, the colocation with UAI and ICML was the best colocation since 1998. And, perhaps best of all, [...]

On the enduring topic of how people deal with intelligent machines, we have this important election bulletin.

I’m not as naturally exuberant as Muthu 2 or David about CS/Econ day, but I believe it and ML day were certainly successful. At the CS/Econ day, I particularly enjoyed Toumas Sandholm’s talk which showed a commanding depth of understanding and application in automated auctions. For the machine learning day, I enjoyed several talks and posters (I [...]

We’d like to invite hunch.net readers to participate in the NIPS 2008 workshop on kernel learning. While the main focus is on automatically learning kernels from data, we are also also looking at the broader questions of feature selection, multi-task learning and multi-view learning. There are no restrictions on the learning problem being addressed [...]

Although I’m greatly interested in machine learning, I think it must be admitted that there is a large amount of low quality logic being used in reviews. The problem is bad enough that sometimes I wonder if the Byzantine generals limit has been exceeded. For example, I’ve seen recent reviews where the given [...]

This workshop asks for insights how far we may/can push the theoretical boundary of using data in the design of learning machines. Can we express our classification rule in terms of the sample, or do we have to stick to a core assumption of classical statistical learning theory, namely that the hypothesis space is to [...]

Claire asked me to be on the SODA program committee this year, which was quite a bit of work. I had a relatively light load—merely 49 theory papers. Many of these papers were not on subjects that I was expert about, so (as is common for theory conferences) I found various reviewers that I trusted [...]

This post is about contextual bandit problems where, repeatedly: The world chooses features x and rewards for each action r1,…,rk then announces the features x (but not the rewards). A policy chooses an action a. The world announces the reward ra The goal in these situations is to learn a policy which maximizes ra in expectation efficiently. I’m [...]

If you are in the New York area and interested in machine learning, consider submitting a 2 page abstract to the ML symposium by tomorrow (Sept 5th) midnight. It’s a fun one day affair on October 10 in an awesome location overlooking the world trade center site. A bit further off (but a real conference) [...]

One way that many conferences in machine learning assign reviewers to papers is via bidding, which has steps something like: Invite people to review Accept papers Reviewers look at title and abstract and state the papers they are interested in reviewing. Some massaging happens, but reviewers often get approximately the papers they bid for. At the ICML business meeting, Andrew [...]

This post is about a technology which could develop in the future. Right now, a new drug might be tested by finding patients with some diagnosis and giving or not giving them a drug according to a secret randomization. The outcome is observed, and if the average outcome for those treated is measurably better than [...]

here on statistics, ML, CS, and other things he knows well.