Visible to the public Biblio

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Joseph Wang, Kirill Trapeznikov, Venkatesh Saligrama.  2014.  An LP for Sequential Learning Under Budgets. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2014, Reykjavik, Iceland, April 22-25, 2014. 33:987–995.
Joseph Wang, Venkatesh Saligrama.  2013.  Locally-Linear Learning Machines (L3M). Asian Conference on Machine Learning, {ACML} 2013, Canberra, ACT, Australia, November 13-15, 2013. 29:451–466.
Joseph Wang, Venkatesh Saligrama.  2012.  Local Supervised Learning through Space Partitioning. Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States.. :91–99.
Jonathan Root, Jing Qian, Venkatesh Saligrama.  2015.  Learning Efficient Anomaly Detectors from K-NN Graphs. Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2015, San Diego, California, USA, May 9-12, 2015. 38
Jacek Cyranka, Md. Ariful Islam, Greg Byrne, Paul L. Jones, Scott A. Smolka, Radu Grosu.  2017.  Lagrangian Reachabililty. Computer Aided Verification - 29th International Conference, {CAV} 2017 Proceedings, Part {I}. :379–400.
Jacek Cyranka, Md. Ariful Islam, Greg Byrne, Paul Jones, Scott A. Smolka, Radu Grosu.  2017.  Lagrangian Reachability. International Conference on Computer Aided Verification (CAV 2017). :379–400.
J. Zhao, C. K. Chang, L. Itti.  2017.  Learning to Recognize Objects by Retaining other Factors of Variation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA. :1-9.

Most ConvNets formulate object recognition from natural images as a single task classification problem, and attempt to learn features useful for object categories, but invariant to other factors of variation such as pose and illumination. They do not explicitly learn these other factors; instead, they usually discard them by pooling and normalization. Here, we take the opposite approach: we train ConvNets for object recognition by retaining other factors (pose in our case) and learning them jointly with object category. We design a new multi-task leaning (MTL) ConvNet, named disentangling CNN (disCNN), which explicitly enforces the disentangled representations of object identity and pose, and is trained to predict object categories and pose transformations. disCNN achieves significantly better object recognition accuracies than the baseline CNN trained solely to predict object categories on the iLab-20M dataset, a large-scale turntable dataset with detailed pose and lighting information. We further show that the pretrained features on iLab-20M generalize to both Washington RGB-D and ImageNet datasets, and the pretrained disCNN features are significantly better than the pretrained baseline CNN features for fine-tuning on ImageNet.

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Giraldo, Jairo, Cardenas, Alvaro, Kantarcioglu, Murat.  2017.  Leveraging Unique CPS Properties to Design Better Privacy-Enhancing Algorithms. Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp. :1–12.
Georgios Giantamidis, Stavros Tripakis.  2016.  Learning Moore Machines from Input-Output Traces. {FM} 2016: Formal Methods - 21st International Symposium, Limassol, Cyprus, November 9-11, 2016, Proceedings. :291–309.
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