*Tufts ML Alumni*

Marta Arias obtained her PhD from Tufts in 2004.

She was advised by Roni Khardon.

**Homepage: **http://www.lsi.upc.edu/~marias/

- M. Arias, R. Khardon and J. Maloberti, Learning Horn Expressions with LogAn-H,
*Journal of Machine Learning Research*, Vol 8, pp549--587 , 2007 [+]

**Authors:**M. Arias, R. Khardon and J. MalobertiJournal of Machine Learning Research

Vol 8, pp549--587**Year:**2007**Url:**http://www.cs.tufts.edu/~roni/PUB/loganh-JMLR.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Arias, M. and Khardon, R., Complexity parameters for first order structures,
*Machine Learning*, Volume 64, pages 121-144, 2006 [+]

**Authors:**Arias, M. and Khardon, R.Machine Learning

Volume 64, pages 121-144**Year:**2006**Url:**http://www.cs.tufts.edu/~roni/PUB/FOVCD-MLJ.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Khardon,R., Arias, M., Servedio, R. A., Polynomial Certificates for Propositional Classes,
*Information and Computation*, vol. 204, pp. 816-834 , 2006 [+]

**Authors:**Khardon,R., Arias, M., Servedio, R. A.Information and Computation

vol. 204, pp. 816-834**Year:**2006**Url:**http://www.cs.tufts.edu/~roni/PUB/Certificates-IC.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - M. Arias and R. Khardon, The Subsumption Lattice and Query Learning,
*Journal of Computer and System Sciences*, Volume 72, Issue 1, Pages 72-94, 2006 [+]

**Authors:**M. Arias and R. KhardonJournal of Computer and System Sciences

Volume 72, Issue 1, Pages 72-94**Year:**2006**Url:**http://www.cs.tufts.edu/~roni/PUB/JCSS-subsumption.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - M. Arias and R. Khardon, Bottom-up ILP using Large Refinement Steps,
*In The Proceedings of the International Conference on Inductive Logic Programming*, pp26-42, 2004 [+]

**Authors:**M. Arias and R. KhardonIn The Proceedings of the International Conference on Inductive Logic Programming

pp26-42**Year:**2004**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - M. Arias and R. Khardon, The Subsumption Lattice and Query Learning,
*In the Proceedings of the International Conference on Algorithmic Learning Theory*, 2004 [+]

**Authors:**M. Arias and R. KhardonIn the Proceedings of the International Conference on Algorithmic Learning Theory

**Year:**2004**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Arias, M. and Khardon, R., Complexity Parameters for First Order Classes,
*International conference on Inductive Logic Programming*, pp. 22-37 , 2003 [+]

**Authors:**Arias, M. and Khardon, R.International conference on Inductive Logic Programming

pp. 22-37**Year:**2003**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Arias, M. and Khardon, R., Learning closed Horn expressions,
*Information and Computation*, vol. 178, pp. 214-240 , 2002 [+]

**Authors:**Arias, M. and Khardon, R.Information and Computation

vol. 178, pp. 214-240**Year:**2002**Url:**http://www.cs.tufts.edu/~roni/PUB/ClosedHorn.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Arias, M. and Khardon, R., A new algorithm for learning range restricted Horn expressions,
*International conference on Inductive Logic Programming*, Springer LNAI, pages 21-39., 2000 [+]

**Authors:**Arias, M. and Khardon, R.International conference on Inductive Logic Programming

Springer LNAI, pages 21-39.**Year:**2000**Url:**http://www.eecs.tufts.edu/~roni/PUB/ILP2K.us.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**

**Current Research Topics:**

- Learning Expressions in First Order Logic [+]

**Description:**We are studying problems where a machine learning system sees examples and produces hypotheses which are represented by logical formulae. This generic framework fits many applications from learning moves for Chess to identifying whether molecules will work well as drugs. Our theoretical work aims to characterize the computational complexity of these problems, i.e. how easy or hard they are to solve. Our work includes developing algorithms which are provably correct and efficient, and proving lower bounds that show limitations on the efficiency of any algorithm for such problems.

This work is partly supported by NSF grant IIS-0099446. - LogAn-H: a system for learning from relational data [+]

**Description:**We have developed a system for learning from relational data (e.g. from graphs). The system is pretty efficient, gives a non traditional type of algorithm ("bottom up") based on the theoretical results, and is a state of the art Inductive Logic Programming (ILP) system. Current work is focused on solving large scale problems involving classification of molecules.

his work is partly supported by NSF grant IIS-0099446. - Complexity of Learning Propositional Formulae [+]

**Description:**This project investigates the resource complexity of learning formulae

in propositional logic. Resources include run time as well as the

number of examples needed to learn. In models where the learner can

ask questions we measure the number of questions needed to complete

the learning task. The focus is on provably correct algorithms and

their analysis.

his work is partly supported by NSF grant IIS-0099446.