There were several papers that seemed fairly interesting at KDD this year. The ones that caught my attention are:
- Xin Jin, Mingyang Zhang, Nan Zhang, and Gautam Das, Versatile Publishing For Privacy Preservation. This paper provides a conservative method for safely determining which data is publishable from any complete source of information (for example, a hospital) such that it does not violate privacy rules in a natural language. It is not differentially private, so no external sources of join information can exist. However, it is a mechanism for publishing data rather than (say) the output of a learning algorithm.
- Arik Friedman Assaf Schuster, Data Mining with Differential Privacy. This paper shows how to create effective differentially private decision trees. Progress in differentially private datamining is pretty impressive, as it was defined in 2006.
- David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, Diane Lambert, Evaluating Online Ad Campaigns in a Pipeline: Causal Models At Scale This paper is about automated estimation of ad campaign effectiveness. The double robust estimation technique seems intuitively appealing and plausibly greatly enhances effectiveness.
- Naoki Abe et al. Optimizing Debt Collections Using Constrained Reinforcement Learning This is an application paper about optimizing the New York State income tax collection agency. As you might expect, there are several cludgy aspects due to working within legal and organizational constraints. They deal with them, and expect to end up making NY state around $108/year. Too bad I live in NY 🙂
- Vikas C Raykar, Balaji Krishnapuram, and Shinpeng Yu Designing Efficient Cascaded Classifiers: Tradeoff between Accuracy and Cost This paper is about a continuization based solution to designing a cost-efficient yet accurate classifier cascade. It’s a step beyond the Viola Jones style boosting with cutouts, but I suspect not yet a final solution.
- D. Sculley, Combined Regression and Ranking. There are lots of applications where you want both a correct ordering and an estimated value of each item. This paper shows a simple combined-loss approach to getting both which empirically improves on either metric.
In general, it seems like KDD is substantially maturing as a conference. The work on empirically effective privacy-preserving algorithms and some of the stats-work is ahead of what I’ve seen at other machine learning conferences. Presumably this is due to KDD being closer to the business side of machine learning and hence more aware of what are real problems there. An annoying aspect of KDD as a publishing venue is that they don’t put the papers on the conference website, due to ACM constraints. A substantial compensation is that all talks are scheduled to appear on videolectures.net and, as you can see, most papers can be found on author webpages.
KDD also experimented with crowdvine again this year so people could announce which talks they were interested in and setup meetings. My impression was that it worked a bit less well than last year, partly because it wasn’t pushed as much by the conference organizers. Small changes in the interface might make a big difference—for example, just providing a ranking of papers by interest might make it pretty compelling.