Gabriel Wachman

PhD Candidate
Department of Computer Science
Tufts University


Research Interests (brief):
Machine Learning
Learning from Structured Data
Learning with Noise
Computational Learning Theory
"Kernels for Periodic Time Series in Astronomy" with with Roni Khardon, Pavlos Protopapas, and Charles Alcock, ECML 2009 (to appear).
"Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs," with Roni Khardon, ICML 2007, PDF
"Relaxed Online Support Vector Machines for Spam Filtering," with D. Sculley. SIGIR 2007
"Noise Tolerant Variants of the Perceptron Algorithm," with Roni Khardon, JMLR 8(Feb):227--248, 2007. PDF
"Spam Classification with On-line Linear Classifiers and Inexact String Matching Features," with D. Sculley and Carla Brodley, Proceedings of The Fifteenth Text Retrieval Conference, 2006.

Research Interests (extended):

Currently my research focuses on learning from structured data. It is often the case that data is most naturally represented structurally, using a graph, for instance. Attempts to propositionalize the data (that is, turn it into a feature vector) make it very difficult to capture the important information contained in the structure. A very popular example of this phenomenon is learning from chemical structures. In this setting, the problem is to predict the behavior of molecules, i.e. their carcinogenicity. Using detailed domain-specific knowledge we can determine the important characteristics of the molecule and give that information to the learner. The problem with this approach is that the bias imposed on the learner by the domain expert may (and in practice seems to) eliminate critical information. Furthermore, there are many domains in which the important characteristics of the data cannot be easily represented by propositional features. Given these challenges, the development of a learner that leverages the important features of structured data with minimal interference from domain experts remains an important open research area. Recent approaches have taken the form of so-called "graph kernels," which are functions that take as input graphs and give as output a real-valued score that allows a learner to differentiate between different kinds of graphs. The construction of such a function is tricky, however, and while such approaches have proved promising, they have not been shown to significantly out-perform existing methods in general.

A second major research area is understanding the effects of margin on learning algorithms. In previous research on noise-tolerant perceptron variants, we discovered that a variant built for finding a large-margin separator performed better in noisy environments than variants built for noise tolerance (i.e. with a L1/L2 slack term). Current learning theory tells us that the upper bound on the error rate of a learner is inversely proportional to the margin, but it falls short of telling us why margin is a "good thing." Looking at the Perceptron algorithm in particular, current proofs of the bounds on various Perceptron variants typically penalize the learner for making margin mistakes. An important contribution to the community would be a proof representing margin updates as improvements.

Contact Information:

Gabriel Wachman
Department of Computer Science
Tufts University
161 College Avenue
Medford, MA

e-mail address: uid: gwachm01 domain: