Tomas Singliar's Patensts and Publications

Business Intelligence / Data Science / Econometrics

♥ T. Singliar, F. Moerchen: DELi: A framework for measuring customer impact of catalog changes, Amazon Machine Learning Conference, April 2015 [Amazon internal publication]

♥ N. Rose, A. Dutta, T. Singliar: A quasi-A/B technique for ASIN experiments, Amazon Machine Learning Conference, April 2015 [Amazon internal publication]

♥ T. Singliar, F. Moerchen: Quantifying impact of sourcing catalog data, Amazon Machine Learning Conference, April 2014 [Amazon internal publication]

Behavior Understanding

♥ T. Singliar, D. Margineantu: Intent estimation method and system for agents of limited perception. US Patent #8,959,042. Amends USP#8,756,177.

♥ T. Singliar, D. Margineantu: Methods and system for estimating subject intent from surveillance, US Patent #8,756,177, issued June 2014

♥ T. Singliar: Monitoring the state-of-health information for components, US Patent #8,533,133, issued Sep 2013

♥ T. Singliar, D. Marginenantu: Scaling up Inverse Reinforcement Learning through Instructed Feature Construction , Snowbird Learning Workshop 2011. [pdf]

Traffic management

The grand scheme of things is to create models that leverage existing data being recorded on the highways to useful ends such as detecting accidents automatically. Routing decisions that you get from MapQuest and the like give you the best expected travel time, marginally. Can we do better if we condition on traffic conditions expected at the actual time of travel? How do we model and predict the "expected conditions"?

♥ M. Hauskrecht, T. Singliar: Monte-Carlo optimization for resource allocation problems in stochastic network systems; UAI2003
[pdf] [BibTex]

♥ T. Singliar, M. Hauskrecht: Towards a Learning Incident Detection System; Workshop on Machine Learning Methods for Surveillance and Event Detection at the International Conference on Machine Learning ICML 2006, Pittsburgh, 2006
[ps] [BibTex] (supplemental figures)

♥ T. Singliar, M. Hauskrecht: Modeling and learning of highway traffic volumes and their interactions, Technical report TR-06-142, Computer Science Dept, University of Pittsburgh, 2006

♥ T. Singliar, M. Hauskrecht: Learning to Detect Adverse Traffic Events from Noisily Labeled Data; Principles and Practice of Knowledge Discovery in Databases, ECML/PKDD 2007 , Warsaw, Poland, 2007
[pdf] [BibTex] [video]
Published and copyrighted by Springer Verlag.

♥ T. Singliar, M. Hauskrecht: Modeling Highway Traffic Volumes; European Conference on Machine Learning, ECML/PKDD 2007 , Warsaw, Poland, 2007
[pdf] poster [BibTex] Published and copyrighted by Springer Verlag.

♥ T. Singliar, M. Hauskrecht: Approximation Strategies for Routing in Dynamic Stochastic Networks; ISAIM 08 - 10th International Symposium on Artificial Intelligence and Mathematics , Ft Lauderdale, FL, 2008
[pdf] [BibTex]

♥ T. Singliar: Machine Learning Tools for Transportation Networks - PhD thesis; [pdf] [BibTex]

Machine Learning / Data mining

♥ T. Singliar, D. Dash: Efficient inference in persistent Dynamic Bayesian Networks; Appeared in UAI-08.
(This paper has its own page.)

The noisy-or component analysis model mines binary data for common causes of link appearance.

♥ M. Hauskrecht, T. Singliar: Variational Learning for Noisy-or Component Analysis; SIAM International Conference on Statistical Data Mining SDM2005, Newport Beach, California.
[pdf] [BibTex]

♥ T. Singliar, M. Hauskrecht: Noisy-Or Component Analysis and Its Application to Link Analysis;
[pdf] [BibTex] Journal of Machine Learning Research - JMLR, (7) 2006

Anomaly detection

By dividing a population of computer hosts into clusters according to features putatively indicative of worm infection susceptibility, one can improve the signal-to-noise ratio. Since we don't have access to those features, we derive the clusters from network behavior patterns.

♥ T. Singliar, D. Dash: COD: Online Temporal Clustering for Outbreak Detection; 22nd Conference on Artificial Intelligence AAAI-07
[pdf] [BibTex]

♥ T. Singliar, D. Dash: Online Temporal Clustering for Outbreak Detection; 6th Annual Conference of the Syndromic Surveillance Society, 2007 ISDS-07
Also appears in Advances in Disease Surveillance, Vol 4 (2007)
[pdf] [BibTex] (poster png)

Petri Nets

Petri Nets are a popular formalism for specification of concurrent systems with a solid theoretical underpinning. This paper is an algebraic characterization of PN models that allow for a concept of synchronization.

♥ G. Juhas, R. Lorenz, T. Singliar: On synchronicity and concurrency in Petri Nets; ATPN 2003