*Faculty*

Roni Khardon is a professor in the Department of Computer Science at Tufts University. He holds a Ph.D. in Computer Science from Harvard University, and M.Sc. and B.Sc. degrees from the Technion. Prior to moving to Tufts he held a faculty position at the University of Edinburgh. Khardon's interests are in Machine Learning and Data Mining, Artificial Intelligence, and Efficient Algorithms and his research explores theoretical questions, empirical questions, and applications. He is serving as an associate editor for the Machine Learning Journal and the Artificial Intelligence Journal, and he has served as an associate editor for the Journal of Artificial Intelligence Research during 2011-2017. He regularly serves on program committees for leading conferences in AI and machine learning.

**CV: **ronicv.pdf

**Homepage: **http://www.cs.tufts.edu/~roni/

- R. Sheth and R. Khardon, Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models,
*Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS)*, 2017 [+]

**Authors:**R. Sheth and R. KhardonProceedings of the Annual Conference on Neural Information Processing Systems (NIPS)

**Year:**2017**Abstract:**Please see our NIPS spotlight video for this paper**Url:**http://www.cs.tufts.edu/~roni/PUB/nips2017-agnosticBayes-final.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - A. Raghavan, S. Sanner, R. Khardon, P. Tadepalli, A. Fern, Hindsight Optimization for Hybrid State and Action MDPs,
*Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI)*, 2017 [+]

**Authors:**A. Raghavan, S. Sanner, R. Khardon, P. Tadepalli, A. FernProceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI)

**Year:**2017**Url:**http://www.cs.tufts.edu/~roni/PUB/aaai17-hsahop.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and S. Sanner, Stochastic Planning and Lifted Inference,
*arXiv*, 1701.01048, 2017 [+]

**Authors:**R. Khardon and S. SannerarXiv

1701.01048**Year:**2017**Url:**http://arxiv.org/abs/1701.01048**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth and R. Khardon, A Fixed-Point Operator for Inference in Variational Bayesian Latent Gaussian Models,
*The 19th International Conference on Artificial Intelligence and Statistics (AISTATS)*, 2016 [+]

**Authors:**R. Sheth and R. KhardonThe 19th International Conference on Artificial Intelligence and Statistics (AISTATS)

**Year:**2016**Url:**http://www.cs.tufts.edu/~roni/PUB/aistats16-fixedpoint.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth and R. Khardon, Monte Carlo Structured SVI for Non-Conjugate Models,
*arXiv*, 1612.03957, 2016 [+]

**Authors:**R. Sheth and R. KhardonarXiv

1612.03957**Year:**2016**Url:**http://arxiv.org/abs/1612.03957**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - H. Cui and R. Khardon, Online Symbolic Gradient-Based Optimization for Factored Action MDPs,
*International Joint Conference on Artificial Intelligence (IJCAI)*, 2016 [+]

**Authors:**H. Cui and R. KhardonInternational Joint Conference on Artificial Intelligence (IJCAI)

**Year:**2016**Url:**http://www.cs.tufts.edu/~roni/PUB/ijcai16-sogbofa.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - H. Cui, R. Khardon, A. Fern, and P. Tadepalli., Factored MCTS for Large Scale Stochastic Planning. ,
*Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)*, 2015 [+]

**Authors:**H. Cui, R. Khardon, A. Fern, and P. Tadepalli.Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)

**Year:**2015**Url:**http://www.cs.tufts.edu/~roni/PUB/aaai15algebraic.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - M. Issakkimuthu, A. Fern, R. Khardon, P. Tadepalli, and S. Xue, Hindsight Optimization for Probabilistic Planning with Factored Actions,
*Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS)*, 2015 [+]

**Authors:**M. Issakkimuthu, A. Fern, R. Khardon, P. Tadepalli, and S. XueProceedings of the International Conference on Automated Planning and Scheduling (ICAPS)

**Year:**2015**Url:**http://www.cs.tufts.edu/~roni/PUB/icaps15hop.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - A. Raghavan, R. Khardon, P. Tadepalli, and A. Fern, Memory-Efficient Symbolic Online Planning for Factored MDPs,
*Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI)*, 2015 [+]

**Authors:**A. Raghavan, R. Khardon, P. Tadepalli, and A. FernProceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI)

**Year:**2015**Url:**http://www.cs.tufts.edu/~roni/PUB/uai2015srtdp.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Sheth, Y. Wang and R. Khardon , Sparse Variational Inference for Generalized Gaussian Process Models,
*Proceedings of the International Conference on Machine Learning (ICML)*, 2015 [+]

**Authors:**R. Sheth, Y. Wang and R. KhardonProceedings of the International Conference on Machine Learning (ICML)

**Year:**2015**Url:**http://www.cs.tufts.edu/~roni/PUB/icml15sparseFPGP.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Benjamin J. Hescott and Roni Khardon, The Complexity of Reasoning with FODD and GFODD,
*Artificial Intelligence*, 2015 [+]

**Authors:**Benjamin J. Hescott and Roni KhardonArtificial Intelligence

**Year:**2015**Abstract:**Journal version is at http://dx.doi.org/10.1016/j.artint.2015.08.005**Url:**http://www.cs.tufts.edu/~roni/PUB/AIJ2015gfoddComplexity.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Benjamin J. Hescott and Roni Khardon, The Complexity of Reasoning with FODD and GFODD,
*Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)*, 2014 [+]

**Authors:**Benjamin J. Hescott and Roni KhardonProceedings of the AAAI Conference on Artificial Intelligence (AAAI)

**Year:**2014**Url:**http://www.cs.tufts.edu/~roni/PUB/aaai2014gfcomplexity.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - G. Garriga, R. Khardon and L. De Raedt , Mining Closed Patterns in Relational, Graph and Network Data,
*Annals of Mathematics and Artificial Intelligence*, Vol 69, Issue 4, pp 315-342, 2013 [+]

**Authors:**G. Garriga, R. Khardon and L. De RaedtAnnals of Mathematics and Artificial Intelligence

Vol 69, Issue 4, pp 315-342**Year:**2013**Url:**http://www.cs.tufts.edu/~roni/PUB/AMAI12-relclosed.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - S Joshi, R Khardon, P Tadepalli, A Fern, A Raghavan, Relational Markov Decision Processes: Promise and Prospects,
*StarAI Workshop help at the Twenty-Seventh AAAI National Conference on Artificial Intelligence (StarAI)*, 2013 [+]

**Authors:**S Joshi, R Khardon, P Tadepalli, A Fern, A RaghavanStarAI Workshop help at the Twenty-Seventh AAAI National Conference on Artificial Intelligence (StarAI)

**Year:**2013**Url:**http://www.cs.tufts.edu/~roni/PUB/xmdp-starai-2013.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. Fern, Solving Relational MDPs with Exogenous Events and Additive Rewards,
*The European Conference on Machine Learning (ECML/PKDD)*, 2013 [+]

**Authors:**S. Joshi, R. Khardon, P. Tadepalli, A. Raghavan, A. FernThe European Conference on Machine Learning (ECML/PKDD)

**Year:**2013**Abstract:**An extended version is available as an arXiv technical report at http://arxiv.org/abs/1306.6302**Url:**http://www.cs.tufts.edu/~roni/PUB/ecml13-xmdp.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - A. Raghavan, R. Khardon, A. Fern, and P. Tadepalli , Symbolic Opportunistic Policy Iteration for Factored-Action MDPs,
*Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS)*, 2013 [+]

**Authors:**A. Raghavan, R. Khardon, A. Fern, and P. TadepalliProceedings of the Annual Conference on Neural Information Processing Systems (NIPS)

**Year:**2013**Url:**http://www.cs.tufts.edu/~roni/PUB/nips13-opi.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - B. Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, and E. Miller., A Discriminative-Generative Approach to the Characterization of Subsurface Contaminant Source Zones.,
*IEEE International Geoscience and Remote Sensing Symposium (IGARSS)*, 2012 [+]

**Authors:**B. Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, and E. Miller.IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

**Year:**2012**Url:**http://www.cs.tufts.edu/~roni/PUB/IGARSS12-moe-labels-gaussian.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - B.Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, and E.Miller., A Mixture of Experts Based Discretization Approach for Characterizing Subsurface Contaminant Source Zones,
*IEEE Statistical Signal Processing Workshop (SSP)*, 2012 [+]

**Authors:**B.Ahmed, I. Mendoza-Sanchez, R. Khardon, L. Abriola, and E.Miller.IEEE Statistical Signal Processing Workshop (SSP)

**Year:**2012**Url:**http://www.cs.tufts.edu/~roni/PUB/SSP12-moe-labels-box.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - D. Kim, P. Protopapas, M. Trichas, M. Rowan-Robinson, R. Khardon, C. Alcock and Y. Byun, A Refined QSO Selection Method Using Diagnostics Tests: 663 QSO Candidates in the LMC ,
*The Astrophysical Journal*, Volume 77, Number 2, 2012 [+]

**Authors:**D. Kim, P. Protopapas, M. Trichas, M. Rowan-Robinson, R. Khardon, C. Alcock and Y. ByunThe Astrophysical Journal

Volume 77, Number 2**Year:**2012**Abstract:**Please see our dataset of time series predicted to be Quasars according to the model in this paper.**Url:**http://www.cs.tufts.edu/~roni/PUB/refinedQSO-ApJ2012.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Saket Joshi, Paul Schermerhorn, Roni Khardon and Matthias Scheutz, Abstract Planning for Reactive Robots,
*IEEE International Conference on Robotics and Automation (ICRA)*, 2012 [+]

**Authors:**Saket Joshi, Paul Schermerhorn, Roni Khardon and Matthias ScheutzIEEE International Conference on Robotics and Automation (ICRA)

**Year:**2012**Url:**http://www.cs.tufts.edu/~roni/PUB/foddrobot-icra12.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Yuyang Wang, Roni Khardon, Dmitry Pechyony and Rosie Jones, Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions,
*The Annual Conference on Learning Theory (COLT)*, 2012 [+]

**Authors:**Yuyang Wang, Roni Khardon, Dmitry Pechyony and Rosie JonesThe Annual Conference on Learning Theory (COLT)

**Year:**2012**Abstract:**An extended version is available as an arXiv technical report at http://arxiv.org/abs/1203.0970**Url:**http://www.cs.tufts.edu/~roni/PUB/colt12-oam.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Yuyang Wang, Roni Khardon, Pavlos Protopapas, Nonparametric Bayesian Estimation of Periodic Lightcurves,
*The Astrophysical Journal*, Vol 756 No 1, pages 67-78, 2012 [+]

**Authors:**Yuyang Wang, Roni Khardon, Pavlos ProtopapasThe Astrophysical Journal

Vol 756 No 1, pages 67-78**Year:**2012**Url:**http://www.cs.tufts.edu/~roni/PUB/ApJ2012-pfind.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - A. Raghavan, S. Joshi, A. Fern, P. Tadepalli, and R. Khardon., Planning in Factored Action Spaces with Symbolic Dynamic Programming.,
*Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)*, 2012 [+]

**Authors:**A. Raghavan, S. Joshi, A. Fern, P. Tadepalli, and R. Khardon.Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)

**Year:**2012**Url:**http://www.cs.tufts.edu/~roni/PUB/aaai12mbfar.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Yuyang Wang and Roni Khardon, Sparse Gaussian Processes for Multi-task Learning,
*The European Conference on Machine Learning (ECML/PKDD)*, 2012 [+]

**Authors:**Yuyang Wang and Roni KhardonThe European Conference on Machine Learning (ECML/PKDD)

**Year:**2012**Abstract:**An extended version is available as an arXiv technical report at http://arxiv.org/abs/1203.0970**Url:**http://www.cs.tufts.edu/~roni/PUB/ecml12-sparsegmt.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Saket Joshi, Kristian Kersting and Roni Khardon, Decision-Theoretic Planning with Generalized First Order Decision Diagrams,
*Artificial Intelligence*, 175 (2011), pp. 2198-2222, 2011 [+]

**Authors:**Saket Joshi, Kristian Kersting and Roni KhardonArtificial Intelligence

175 (2011), pp. 2198-2222**Year:**2011**Url:**http://www.cs.tufts.edu/~roni/PUB/AIJ11-GFODD.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - S. Joshi and R. Khardon, Probabilistic Relational Planning with First Order Decision Diagrams,
*Journal of AI Research*, Volume 41, Pages 231-266, 2011 [+]

**Authors:**S. Joshi and R. KhardonJournal of AI Research

Volume 41, Pages 231-266**Year:**2011**Url:**http://www.cs.tufts.edu/~roni/PUB/JAIR11-foddplanner.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - D. Kim, P. Protopapas, Y. Byun, C. Alcock, R. Khardon and M. Trichas, Quasi-stellar Object Selection Algorithm Using Time Variability and Machine Learning: Selection of 1620 Quasi-stellar Object Candidates from MACHO Large Magellanic Cloud Database,
*The Astrophysical Journal*, Volume 735, Number 2, Pages 68-, 2011 [+]

**Authors:**D. Kim, P. Protopapas, Y. Byun, C. Alcock, R. Khardon and M. TrichasThe Astrophysical Journal

Volume 735, Number 2, Pages 68-**Year:**2011**Abstract:**Please see our dataset of time series predicted to be Quasars according to the model in this paper.**Url:**http://www.cs.tufts.edu/~roni/PUB/QSOApJ2011.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - A. Fern, R. Khardon and P. Tadepalli, The First Learning Track of the International Planning Competition,
*Machine Learning*, Volume 84, Pages 81-107, 2011 [+]

**Authors:**A. Fern, R. Khardon and P. TadepalliMachine Learning

Volume 84, Pages 81-107**Year:**2011**Url:**http://www.cs.tufts.edu/~roni/PUB/IPC-MLJ2011.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Dan Preston, Carla Brodley, Roni Khardon, Damien Sulla-Menashe, and Mark Friedl, Redefining Class Definitions using Constraint-Based Clustering,
*The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)*, 2010 [+]

**Authors:**Dan Preston, Carla Brodley, Roni Khardon, Damien Sulla-Menashe, and Mark FriedlThe ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

**Year:**2010**Url:**http://www.cs.tufts.edu/~roni/PUB/kdd2010-cppc.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Chenggang Wang and Roni Khardon, Relational Partially Observable MDPs,
*In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI)*, 2010 [+]

**Authors:**Chenggang Wang and Roni KhardonIn Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI)

**Year:**2010**Url:**http://www.cs.tufts.edu/~roni/PUB/aaai2010-rpomdp.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Saket Joshi, Kristian Kersting, and Roni Khardon, Self-Taught Decision Theoretic Planning with First Order Decision Diagrams,
*Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS)*, 2010 [+]

**Authors:**Saket Joshi, Kristian Kersting, and Roni KhardonProceedings of the International Conference on Automated Planning and Scheduling (ICAPS)

**Year:**2010**Url:**http://www.cs.tufts.edu/~roni/PUB/icaps10-mcreductions.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Yuyang Wang, Roni Khardon, Pavlos Protopapas, Shift-invariant Grouped Multi-task Learning for Gaussian Processes,
*The European Conference on Machine Learning (ECML/PKDD)*, 2010 [+]

**Authors:**Yuyang Wang, Roni Khardon, Pavlos ProtopapasThe European Conference on Machine Learning (ECML/PKDD)

**Year:**2010**Abstract:**An extended version is available as an arXiv technical report at http://arxiv.org/abs/1203.0970**Url:**http://www.cs.tufts.edu/~roni/PUB/ecml10-gmt.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - Roni Khardon, Stochastic Planning and Lifted Inference,
*AAAI10 workshop on Statistical Relational AI*, 2010 [+]

**Authors:**Roni KhardonAAAI10 workshop on Statistical Relational AI

**Year:**2010**Url:**http://www.cs.tufts.edu/~roni/PUB/starai2010-sdpinference.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Saket Joshi, Kristian Kersting, and Roni Khardon, Generalized First Order Decision Diagrams for First Order Markov Decision Processes,
*In the Proceedings of the International Joint Conference on Artificial Intelligence*, 2009 [+]

**Authors:**Saket Joshi, Kristian Kersting, and Roni KhardonIn the Proceedings of the International Joint Conference on Artificial Intelligence

**Year:**2009**Url:**http://www.cs.tufts.edu/~roni/PUB/ijcai09-GFODD.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - G. Wachman, R. Khardon, P. Protopapas, and C. Alcock, Kernels for Periodic Time Series Arising in Astronomy,
*Proceedings of the European Conference on Machine Learning (ECML)*, 2009 [+]

**Authors:**G. Wachman, R. Khardon, P. Protopapas, and C. AlcockProceedings of the European Conference on Machine Learning (ECML)

**Year:**2009**Url:**http://www.cs.tufts.edu/~roni/PUB/ecml09-tskernels.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - C. Wang, S. Joshi and R. Khardon, First Order Decision Diagrams for Relational MDPs,
*Journal of AI Research*, Vol 31, pp431-472, 2008 [+]

**Authors:**C. Wang, S. Joshi and R. KhardonJournal of AI Research

Vol 31, pp431-472**Year:**2008**Url:**http://www.cs.tufts.edu/~roni/PUB/fomdp-jair08.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - S. Joshi and R. Khardon, Stochastic Planning with First Order Decision Diagrams,
*Proceedings of the International Conference on Automated Planning and Scheduling*, 2008 [+]

**Authors:**S. Joshi and R. KhardonProceedings of the International Conference on Automated Planning and Scheduling

**Year:**2008**Url:**http://www.cs.tufts.edu/~roni/PUB/icaps08-fodd.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - C. Wang, S. Joshi and R. Khardon, First Order Decision Diagrams for Relational MDPs,
*In the proceedings of the International Joint Conference on Artificial Intelligence*, 2007 [+]

**Authors:**C. Wang, S. Joshi and R. KhardonIn the proceedings of the International Joint Conference on Artificial Intelligence

**Year:**2007**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - G. Wachman and R. Khardon , Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs,
*In the proceedings of the International Conference on Machine Learning.*, 2007 [+]

**Authors:**G. Wachman and R. KhardonIn the proceedings of the International Conference on Machine Learning.

**Year:**2007**Url:**http://www.cs.tufts.edu/~roni/PUB/icml07-Hkernels.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - 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:** - R. Khardon and G. Wachman, Noise Tolerant Variants of the Perceptron Algorithm,
*Journal of Machine Learning Research*, Vol 8, pp227--248, 2007 [+]

**Authors:**R. Khardon and G. WachmanJournal of Machine Learning Research

Vol 8, pp227--248**Year:**2007**Url:**http://www.cs.tufts.edu/~roni/PUB/PVariants-JMLR.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:** - G. Garriga, R. Khardon and L. De Raedt, On Mining Closed Sets in Multi-Relational Data,
*In the proceedings of the International Joint Conference on Artificial Intelligence.*, 2007 [+]

**Authors:**G. Garriga, R. Khardon and L. De RaedtIn the proceedings of the International Joint Conference on Artificial Intelligence.

**Year:**2007**Url:**http://www.cs.tufts.edu/~roni/PUB/ijcai07-closed.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - C. Wang and R. Khardon , Policy Iteration for Relational MDPs,
*In the proceedings of the Conference on Uncertainty in Artificial Intelligence*, 2007 [+]

**Authors:**C. Wang and R. KhardonIn the proceedings of the Conference on Uncertainty in Artificial Intelligence

**Year:**2007**Url:**http://www.cs.tufts.edu/~roni/PUB/uai07-PI.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:** - Khardon, R., D. Roth, R. A. Servedio, Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms,
*Journal of Artificial Intelligence Research*, vol. 24 , pp. 341-356, 2005 [+]

**Authors:**Khardon, R., D. Roth, R. A. ServedioJournal of Artificial Intelligence Research

vol. 24 , pp. 341-356**Year:**2005**Url:**http://www.cs.tufts.edu/~roni/PUB/JAIR.khardon05a.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Servedio, R., Maximum Margin Algorithms with Boolean Kernels,
*Journal of Machine Learning Research*, vol. 6 pp. 1405-1429, 2005 [+]

**Authors:**Khardon, R., Servedio, R.Journal of Machine Learning Research

vol. 6 pp. 1405-1429**Year:**2005**Url:**http://www.cs.tufts.edu/~roni/PUB/JMLR.khardon05a.pdf**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - 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:** - N. Abe, R. Khardon and T. Zeugmann (Editors), Special Issue Devoted to Papers from ALT 2001,
*Theoretical Computer Science*, Volume 313, Issue 2, Pages 173-312, 2004 [+]

**Authors:**N. Abe, R. Khardon and T. Zeugmann (Editors)Theoretical Computer Science

Volume 313, Issue 2, Pages 173-312**Year:**2004**Url:**http://www.sciencedirect.com/science/journal/03043975**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - 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:** - Gunopulos, D., Khardon, R., Mannila, H., Saluja, S. , Toivonen, H., Sharma, R.S., Discovering All Most Specific Sentences,
*ACM Transactions on Database Systems*, vol. 28, 2, 2003 [+]

**Authors:**Gunopulos, D., Khardon, R., Mannila, H., Saluja, S. , Toivonen, H., Sharma, R.S.ACM Transactions on Database Systems

vol. 28, 2**Year:**2003**Url:**http://www.cs.tufts.edu/~roni/PUB/TODStrans.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Servedio, R., Maximum Margin Algorithms with Boolean Kernels,
*International conference on Computational Learning Theory*, pp. 87-101, 2003 [+]

**Authors:**Khardon, R., Servedio, R.International conference on Computational Learning Theory

pp. 87-101**Year:**2003**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Arias, M., Khardon, R., Servedio, R., Polynomial Certificates for Propositional Classes,
*International conference on Computational Learning Theory*, pp. 537-551, 2003 [+]

**Authors:**Arias, M., Khardon, R., Servedio, R.International conference on Computational Learning Theory

pp. 537-551**Year:**2003**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Roth, D., Servedio, R. A., Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms,
*Advances in Neural Information Processing Systems 14*, pp. 423-430 MIT Press , 2002 [+]

**Authors:**Khardon, R., Roth, D., Servedio, R. A.Advances in Neural Information Processing Systems 14

pp. 423-430 MIT Press**Year:**2002**Url:**http://www.cs.tufts.edu/~roni/**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - 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:** - N. Abe, R. Khardon and T. Zeugmann (Editors)., Proceedings of the 12th International Conference on Algorithmic Learning Theory, , Springer LNAI 2225, 2001 [+]

**Authors:**N. Abe, R. Khardon and T. Zeugmann (Editors).

Springer LNAI 2225**Year:**2001**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - 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:** - Khardon, R., Learning Horn Expressions with LogAn-H,
*Proceedings of the International Conference on Machine Learning*, pp. 471-478, 2000 [+]

**Authors:**Khardon, R.Proceedings of the International Conference on Machine Learning

pp. 471-478**Year:**2000**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Recent Progress in Learning Horn Expressions with Queries,
*Workshop Notes. Machine Intelligence 17*, 2000 [+]

**Authors:**Khardon, R.Workshop Notes. Machine Intelligence 17

**Year:**2000**Url:**http://www.cs.tufts.edu/~roni/PUB/MI17.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Learning Action Strategies for Planning Domains,
*Artificial Intelligence*, vol. 113 pp. 125-148 , 1999 [+]

**Authors:**Khardon, R.Artificial Intelligence

vol. 113 pp. 125-148**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/l2aexppub.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Learning Function Free Horn Expressions,
*Machine Learning*, vol. 37, 1999 [+]

**Authors:**Khardon, R.Machine Learning

vol. 37**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/FFHorn.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Learning Range-Restricted Horn Expressions,
*Proceedings of the Fourth European Conference on Computational Learning Theory*, pp. 111-125 , 1999 [+]

**Authors:**Khardon, R.Proceedings of the Fourth European Conference on Computational Learning Theory

pp. 111-125**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/ec99post.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Learning to Reason with a Restricted View,
*Machine Learning*, vol. 35, 2, pp. 95-117, 1999 [+]

**Authors:**R. Khardon and D. RothMachine Learning

vol. 35, 2, pp. 95-117**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/partial.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Learning to Take Actions,
*Machine Learning*, vol. 35, 1, pp. 57-90 , 1999 [+]

**Authors:**Khardon, R.Machine Learning

vol. 35, 1, pp. 57-90**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/l2a.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Mannila, H., Roth, D., Reasoning with Examples: Propositional Formulae and Database Dependencies,
*Acta Informatica*, pp. 267-286, 1999 , 1999 [+]

**Authors:**Khardon, R., Mannila, H., Roth, D.Acta Informatica

pp. 267-286, 1999**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/db.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., D. Roth, L. G. Valiant, Relational Learning for NLP using Linear Threshold Elements,
*Proceedings of the International Joint Conference of Artificial Intelligence*, pp. 911-917 , 1999 [+]

**Authors:**Khardon, R., D. Roth, L. G. ValiantProceedings of the International Joint Conference of Artificial Intelligence

pp. 911-917**Year:**1999**Url:**http://www.cs.tufts.edu/~roni/PUB/rel-ijcai99.us.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Khardon, R., Learning First Order Universal Horn Expressions,
*International conference on Computational Learning Theory*, pp. 154-165 1998 , 1998 [+]

**Authors:**Khardon, R.International conference on Computational Learning Theory

pp. 154-165 1998**Year:**1998**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Aizenstein, H, Blum, A., Khardon, R., Kushilevitz, A., Pitt, L., Roth, D., On learning read-k satisfy-j DNF,
*SIAM Journal of Computing*, vol. 27, 6, pp. 1505-1530 , 1998 [+]

**Authors:**Aizenstein, H, Blum, A., Khardon, R., Kushilevitz, A., Pitt, L., Roth, D.SIAM Journal of Computing

vol. 27, 6, pp. 1505-1530**Year:**1998**Url:**http://www.cs.tufts.edu/~roni/PUB/rksj.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon, L2Act: User Manual,
*Technical Report, TR-10-97, Harvard University*, 1997 [+]

**Authors:**R. KhardonTechnical Report, TR-10-97, Harvard University

**Year:**1997**Url:**http://www.cs.tufts.edu/~roni/PUB/tr-10-97.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Gunopulos, D., Khardon, R., Mannila, H. , Toivonen, H., Data Mining, Hypergraph Transversals, and Machine Learning,
*Proceedings of the symposium on Principles of Database Systems*, pp. 209-216 , 1997 [+]

**Authors:**Gunopulos, D., Khardon, R., Mannila, H. , Toivonen, H.Proceedings of the symposium on Principles of Database Systems

pp. 209-216**Year:**1997**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Default and Relevance in Model based Reasoning,
*Artificial Intelligence*, vol. 97 pp. 169-193 , 1997 [+]

**Authors:**R. Khardon and D. RothArtificial Intelligence

vol. 97 pp. 169-193**Year:**1997**Url:**http://www.cs.tufts.edu/~roni/PUB/relevance.ps**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Learning to Reason,
*Journal of the ACM*, vol. 44, 5 , pp. 697-725, 1997 [+]

**Authors:**R. Khardon and D. RothJournal of the ACM

vol. 44, 5 , pp. 697-725**Year:**1997**Url:**http://www.cs.tufts.edu/~roni/PUB/l2r.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon, Learning to Take Actions,
*Proceedings of the National Conference on Artificial Intelligence 1996*, pp. 787-792, 1996 [+]

**Authors:**R. KhardonProceedings of the National Conference on Artificial Intelligence 1996

pp. 787-792**Year:**1996**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and S. S. Pinter, Partitioning and scheduling to counteract overhead,
*Parallel Computing*, vol. 22, pp. 555-593 , 1996 [+]

**Authors:**R. Khardon and S. S. PinterParallel Computing

vol. 22, pp. 555-593**Year:**1996**Url:**http://www.cs.tufts.edu/~roni/PUB/grain.ps**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Reasoning with Models,
*Artificial Intelligence*, vol. 87, 1-2, pp. 187-213 , 1996 [+]

**Authors:**R. Khardon and D. RothArtificial Intelligence

vol. 87, 1-2, pp. 187-213**Year:**1996**Url:**http://www.cs.tufts.edu/~roni/PUB/models.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Default-Reasoning with Models,
*Proceedings of the International Joint Conference of Artificial Intelligence 1995*, pp. 319-325, 1995 [+]

**Authors:**R. Khardon and D. RothProceedings of the International Joint Conference of Artificial Intelligence 1995

pp. 319-325**Year:**1995**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Learning to Reason with a Restricted View,
*International conference on Computational Learning Theory 1995*, pp. 301-310 , 1995 [+]

**Authors:**R. Khardon and D. RothInternational conference on Computational Learning Theory 1995

pp. 301-310**Year:**1995**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon, Translating between Horn Representations and their Characteristic Models,
*Journal of Artificial Intelligence Research*, vol. 3, pp. 349-372 , 1995 [+]

**Authors:**R. KhardonJournal of Artificial Intelligence Research

vol. 3, pp. 349-372**Year:**1995**Url:**http://www.cs.tufts.edu/~roni/PUB/JAIR.khardon95a.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon, On Using the Fourier Transform to learn Disjoint DNF,
*Information Processing Letters*, Vol 49, pp. 219-222., 1994 [+]

**Authors:**R. KhardonInformation Processing Letters

Vol 49, pp. 219-222.**Year:**1994**Url:**http://www.cs.tufts.edu/~roni/PUB/fourier.ps**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Learning to Reason,
*Proceedings of the National Conference on Artificial Intelligence*, pp. 682-687, 1994 [+]

**Authors:**R. Khardon and D. RothProceedings of the National Conference on Artificial Intelligence

pp. 682-687**Year:**1994**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - Blum, A., Khardon, R., Kushilevitz, A., Pitt, L., Roth, D., On learning read-k satisfy-j DNF,
*International conference on Computational Learning Theory 1994*, pp. 110-117, 1994 [+]

**Authors:**Blum, A., Khardon, R., Kushilevitz, A., Pitt, L., Roth, D.International conference on Computational Learning Theory 1994

pp. 110-117**Year:**1994**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and D. Roth, Reasoning with Models,
*Proceedings of the National Conference on Artificial Intelligence*, pp. 1148-1153, 1994 [+]

**Authors:**R. Khardon and D. RothProceedings of the National Conference on Artificial Intelligence

pp. 1148-1153**Year:**1994**Associated Research Topics:****Affiliated Tufts Members:****Tufts / Purdue Alumni:**None - R. Khardon and S. S. Pinter, Choosing the Right Grains for Data Flow Machines,
*International Conference on Parallel Processing*, pp. I672-I673 , 1991 [+]

**Authors:**R. Khardon and S. S. PinterInternational Conference on Parallel Processing

pp. I672-I673**Year:**1991**Affiliated Tufts Members:****Tufts / Purdue Alumni:**None

**Current Research Topics:**

- Learning to Act in Relational Markov Decision Processes [+]

**Description:**Markov decision processes give a mathematical model for agents acting in a dynamic environment. The actions of the agent affect the world, its own state, and whether it is "rewarded" or not. However, the results of actions are not deterministic.The basics of this framework are well understood but the main challenge is in scalability to problems with large state spaces and/or action spaces. Our main interest is in developing efficient agents that learn and act in such domains. Our solutions take advantage of structure (relational or propositionally factored) in the state and action space to yield effective solutions.

This work is partly supported by NSF grants IIS-0936687, IIS 0964457 and IIS-1616280 - Graphical Models: Theory, Algorithms and Applications [+]

**Description:**Our work is done in the context of expressive Bayesian probabilistic models (a.k.a graphical models), developing inference algorithms for them, developing a learning theory that explains why these algorithms work and applying them in interesting applications. Our theoretical results provide distribution-free guarantees on the risk of approximate Bayesian inference algorithms. Recent models include constrained clustering, multi-task learning, sparse Gaussian processes, mixture of expert models for label discretization, matrix facorization and topic models. Recent applications include land-cover clustering and classification, analysis of time series from Astronomy, and predicting contamination level in environmental engineering.

This work is partly supported by NSF grants IIS-1714440 and IIS-0803409

- 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. - Mining Frequent Patterns [+]

**Description:**The problem of "mining frequent patterns" is one of the most widely studied problems in data mining. Here, given a database capturing some activities (e.g. shopping lists in supermarkets) one searches for patterns of activities (e.g. sets of items bought together) which occur with sufficiently high frequency. This idea has been applied in a variety of real world problems. Our previous work developed formal complexity results for itemset mining, by showing an equivalence to some problems of learning by asking questions, and developed a new algorithm which is particularly efficient when patterns sought are large. Current work is focused on mining in relational data. For example given a set of molecules described by graphs (capturing a family of interest e.g. "high solubility", or "effective drug") one would like to find frequent substructures in this family. Our Learning to Act system includes one of the earliest implementations of relational frequent set mining.

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. - Learning to Reason [+]

**Description:**The goal in this project is to develop an understanding of system that learn knowledge and use it for reasoning. Most of our work dealt with propositional logic as the underlying reasoning language. The work includes developing different representation formalisms, reasoning algorithms that use them, and algorithms for learning such representations. Our Learning to Reason results demonstrate (in a technical sense) that it can be easier to learn in order to reason (on some things) than learn a model of the world and reason from it. - On Line Learning Algorithms and Kernel Methods [+]

**Description:**On-line learning is an important paradigm where training examples appear one at a time and are not saved for batch processing. This is important for efficiency, and increasingly for suitability for systems that continuously interact with their environment. The well studied Perceptron algorithm is a classical example of an on-line learning algorithm, where SVM is the corresponding batch algorithm. Our work investigates on-line learning algorithms and their theoretical and empirical generalization performance. We have also investigated developing kernels for complex structured data and using these with on-line and batch algorithms.

This work is partly supported by NSF grants IIS-0803409 and IIS-0099446. - Constraint-Based Clustering [+]

**Description:**We are working with the Department of Geography at Boston University on the problem of defining the classes for land cover classification. Our method used both the unsupervised data and labels created from a previous classification scheme.

This work is partly supported by NSF grant IIS-0803409 - Time Series Data Mining [+]

**Description:**We have multiple projects looking at several aspects of mining and learning with time series data with direct applications in several domains including astrophysics and predictive medicine. Recent projects include detecting anomalous time series using novel and efficient clustering algorithms, clustering and classification of time series data using kernel methods and Gaussian processes, and fast search and analysis algorithms detecting events within time series.

This work is partly supported by NSF grants IIS-0803409 and IIS-0713259.

**Associated Data/Software:**

- LogAn-H [+]

**Description:****LogAn-H**is a system for learning function free Horn expressions. It is based on provably correct algorithms for learning with queries. More information can be found in:

Theoretical background (correctness and complexity proofs)

Paper describing the system and some experiments

An Example Run

Two variants of the system and algorithms have been implemented (in Prolog and C) as described in the papers.

Data files and code are available (for research purposes) upon request.**Associated People:****Associated Research:** - L2Act [+]

**Description:**The

**L2Act**system is a learning system that takes as input a description of an AI planning domain and a set of solved insta

nces from that domain (the examples), and induces a set of rules ordered as a decision list that can be used as a reactive planner in this domain.

More information can be found in:

L2Act user manual

Paper describing experiments with the system

Paper describing relevant theoretical results

Data files and code are available (for research purposes) upon request.

The README file describes what programs and data files are available and how to use them.**Associated People:****Associated Research:** - FODD and GFODD based Planners [+]

**Description:**FODD-Planner is a Prolog based software for stochastic planning problems based on the FODD representation developed by our group. The code implements symbolic dynamic programming using our representation of first order decision diagrams.

For more information see project page Learning to Act in Relational MDPs

Two systems are available under this heading.

The most recent version of the FODD based system (includes code for JAIR2011, ICRA2012 and parallelization) is hosted as a google code project at: foddplanner google code site

The code below provides the version of the code using the theorem proving reductions that can be used to reproduce the results from our JAIR 2011 paper.

The most recent version of the GFODD based system (includes code for ECML2013 and more) is hosted as a google code project at: gfood planner google code site**Associated People:****Associated Research:****Download File:**http://www.cs.tufts.edu/~roni/DISTRIB/foddplanner-jair11.tgz - OGLE II Dataset [+]

**Description:**The data in this archive includes the "OGLE II" dataset as used by the machine learning group at Tufts University. The dataset includes time series of light measurements from 3 type of periodic variable stars from the Optical Gravitational Lensing Experiment (OGLE) survey. The data includes a total of 14087 time series with (3425,3390,7272) in the categories (CEPH, RRL, EB).

The data was generated and kindly provided by other researchers. Please see the official OGLE site for more information on the survey, data and discoveries. Various queries on this and other astronomy data can also be made there as well as at the visier site and Harvard time series site.**If using this data please cite the original work**(Szymanski, 2005, Acta Astron., 55, 43 and Udalski, Kubiak and Szymanski, 1997, Acta Astron., 47, 319.) as suggested on the OGLE site.

Our group has made use of this data in machine learning research for anomaly detection, classification, probabilistic modeling and period detection. Please see our time series project page and additional publications therein for more information about the dataset and tasks studied.

We provide the OGLE-II dataset in order to make it more readily accessible to machine learning researchers. Toward that we packaged three versions of the data, as linked below. Please consult our papers for more information about the data, its processing and experiments.

(1) The raw data: the original time series that are measured at irregular time points and are not folded. We also provide the folded versions, as well as the known period (and other properties) as found by the OGLE project: ogle2full.tar.gz

(2) A processed form of the data: each time series is folded according to its known period, and then re-sampled via interpolation at 50 regular sampling time points. Two versions are provided, the time series "as is" and after "universal phasing". This form of the data can be simply treated as a point in 50-D Euclidean space and used directly by machine learning algorithms - providing an easy starting point to study the data. ogle50.libsvm and upogle50.libsvm**Associated People:****Associated Research:** - QSO: predicted Quasars [+]

**Description:**This dataset includes time series that are predicted to be QSOs by the machine learning models in our papers in:

ApJ 2011 , and ApJ 2012. Actual data and more information is hosted on the Harvard site linked below.**Associated People:****Associated Research:****Download File:**http://timemachine.iic.harvard.edu/coati/QSOs/