Lily Mihalkova's Webpage

My general areas of research are artificial intelligence and machine learning. My research interests are in statistical relational learning and reasoning under uncertainty. I have worked on structure learning in relational domains, efficient inference, collective classification, active learning, transfer learning across relational tasks, and applications to problems on the Web.

I spent the summer of 2007 at Microsoft Research where I worked with Matt Richardson.

I co-organized the the AAAI-10 Workshop on Statistical Relational AI.

Publications

PhD Thesis

  • Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation. Also appears as AI Lab Tech Report Number AI09-05. Computer Science Department, University of Texas at Austin. August 2009.
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Papers

  • Lifted Graphical Models: A Survey. Lilyana Mihalkova and Lise Getoor. Submited to the Machine Learning Journal.
    [Tech Report Version PDF] Send us feedback here!

  • Structure Selection from Streaming Data. Lilyana Mihalkova and Walaa Eldin Moustafa. Unpublished manuscript, 2011.
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  • Learning to Predict Web Collaborations. Lilyana Mihalkova, Walaa Eldin Moustafa, and Lise Getoor. At the Workshop on User Modeling for Web Applications (UMWA-11), Hong Kong, February 2011.
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  • Active Inference for Retrieval in Camera Networks. Daozheng Chen, Mustafa Bilgic, Lise Getoor, David Jacobs, Lilyana Mihalkova, and Tom Yeh. At the Workshop on Person-Oriented Vision (POV-11), Kona, HI, January 2011.
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  • Probabilistic Similarity Logic. Matthias Broecheler, Lilyana Mihalkova, and Lise Getoor. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI-10). Catalina Island, CA, July 2010.
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  • Active Learning for Networked Data. Mustafa Bilgic, Lilyana Mihalkova, and Lise Getoor. In Proceedings of the 27th International Conference on Machine Learning (ICML-10). Haifa, Israel, June 2010.
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  • Learning to Disambiguate Search Queries from Short Sessions. Lilyana Mihalkova and Raymond Mooney. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-09). Bled, Slovenia, September 2009.
    [PDF] [Video]
    [Workshop Version PDF]
    [Workshop Version Video]

  • Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals. Lilyana Mihalkova and Matthew Richardson. In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09). Leuven, Belgium, July 2009.
    [Short version PDF] [Long version PDF]
    [Technical Report PDF]

  • Transfer Learning from Minimal Target Data by Mapping across Relational Domains. Lilyana Mihalkova and Raymond Mooney. In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09). Pasadena, CA, July 2009.
    [PDF] [Resources]
    [Workshop Version PDF]

  • Bottom-Up Learning of Markov Logic Network Structure. Lilyana Mihalkova and Raymond Mooney. In Proceedings of the 24th International Conference on Machine Learning (ICML-07). pp. 625-632. Corvallis, OR. June 2007.
    [PDF] [Code]
  • Mapping and Revising Markov Logic Networks for Transfer Learning. Lilyana Mihalkova, Tuyen Huynh, and Raymond Mooney. In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07). pp. 608-614. Vancouver, BC. July 2007
    [PDF] [Code]
  • [Workshop Version PDF]

  • Using Active Relocation to Aid Reinforcement Learning. Lilyana Mihalkova and Raymond Mooney. In Proceedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.
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  • Experiments on Ensembles with Missing and Noisy Data. Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney. In Proceedings of 5th International Workshop on Multiple Classifier Systems (MCS-2004), LNCS Vol. 3077, pp. 293-302, Cagliari, Italy, Springer Verlag, June 2004.
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  • Using Java to Teach Networking Using a Programmable Network Sniffer M. Jipping, A. Bugaj, L. Mihalkova, and D. Porter. In Proceedings of the 2003 SIGCSE Technical Symposium on Computer Science Education, Vol. 35, No. 1, March 2003.