Publications

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  2018 (5)
Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild. Masi, I.; Chang, F.; Choi, J.; Harel, S.; Kim, J.; Kim, K.; Leksut, J.; Rawls, S.; Wu, Y.; Hassner, T.; AbdAlmageed, W.; Medioni, G.; Morency, L.; Natarajan, P.; and Nevatia, R. IEEE Trans. on Pattern Analysis and Machine Intelligence. January 2018.
Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild [link]Paper   link   bibtex  
CapsuleGAN: Generative Adversarial Capsule Network. Jaiswal, A.; AbdAlmageed, W.; and Natarajan, P. In preprint: ArXiv, 2018.
CapsuleGAN: Generative Adversarial Capsule Network [link]Paper   link   bibtex  
Bidirectional Conditional Generative Adversarial Networks. Jaiswal, A.; AbdAlmageed, W.; Wu, Y.; and Natarajan, P. 2018.
Bidirectional Conditional Generative Adversarial Networks [link]Paper   link   bibtex  
Image Copy-Move Forgery Detection via an End-to-End Deep Neural Network. Wu, Y.; AbdAlmageed, W.; and Natarajan, P. In IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
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How To Efficiently Increase Resolution in Neural OCR Models. Rawls, S.; Cao, H.; Mathai, J.; and Natarajan, P. 2018 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR). 2018.
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  2017 (16)
Adversarial Auto-Encoders For Speech Based Emotion Recognition. Sahu, S.; Gupta, R.; Sivaraman, G.; Espy-Wilson, C.; and AbdAlmageed, W. In InterSpeech, 2017.
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Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images and Text. Jaiswal, A.; Sabir, E.; AbdAlmageed, W.; and Natarajan, P. 2017.
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Deep Matching and Validation Network: An End-to-End Solution to Constrained Image Splicing Localization and Detection. Wu, Y.; AbdAlmageed, W.; and Natarajan, P. In Proceedings of the 2017 ACM on Multimedia Conference, pages 1480–1502, 2017. ACM
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Designing hyperchaotic cat maps with any desired number of positive Lyapunov exponents. Hua, Z.; Yi, S.; Zhou, Y.; Li, C.; and Wu, Y. IEEE transactions on cybernetics. 2017.
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EPAT: Euclidean Perturbation Analysis and Transform-An Agnostic Data Adaptation Framework for Improving Facial Landmark Detectors. Wu, Y.; AbdAlmageed, W.; Rawls, S.; and Natarajan, P. In Automatic Face & Gesture Recognition (FG 2017), 2017 12th IEEE International Conference on, pages 222–229, 2017. IEEE
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Self-organized Text Detection with Minimal Post-processing via Border Learning. Wu, Y.; and Natarajan, P. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5000–5009, 2017.
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Combining Convolutional Neural Networks and LSTMs for Segmentation-Free OCR. Rawls, S.; Cao, H.; Kumar, S.; and Natarajan, P. IAPR International Conference on Document Analysis and Recognition (ICDAR),155–160. 2017.
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Facial landmark detection with tweaked convolutional neural networks. Wu, Y.; Hassner, T.; Kim, K.; Medioni, G.; and Natarajan, P. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.
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Combining deep learning and language modeling for segmentation-free OCR from raw pixels. Rawls, S.; Cao, H.; Sabir, E.; and Natarajan, P. In 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), pages 119–123, April 2017.
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SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation. May, J.; and Priyadarshi, J. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 536–545, Vancouver, Canada, August 2017. Association for Computational Linguistics
SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation [link]Paper   link   bibtex   abstract  
Cross-lingual Name Tagging and Linking for 282 Languages. Pan, X.; Zhang, B.; May, J.; Nothman, J.; Knight, K.; and Ji, H. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1946–1958, Vancouver, Canada, July 2017. Association for Computational Linguistics
Cross-lingual Name Tagging and Linking for 282 Languages [link]Paper   link   bibtex   abstract  
Unifying Local and Global Change Detection in Dynamic Networks. Li, W.; Guo, D.; Steeg, G. V.; and Galstyan, A. CoRR, abs/1710.03035. 2017.
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Low Complexity Gaussian Latent Factor Models and a Blessing of Dimensionality. Steeg, G. V.; and Galstyan, A. CoRR, abs/1706.03353. 2017.
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Multitask Learning and Benchmarking with Clinical Time Series Data. Harutyunyan, H.; Khachatrian, H.; Kale, D. C.; and Galstyan, A. CoRR, abs/1703.07771. 2017.
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Sifting Common Information from Many Variables. Steeg, G. V.; Gao, S.; Reing, K.; and Galstyan, A. In IJCAI, 2017.
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Disentangled representations via synergy minimization. Steeg, G. V.; Brekelmans, R.; Harutyunyan, H.; and Galstyan, A. 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton),180–187. 2017.
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  2016 (18)
Face recognition using deep multi-pose representations. AbdAlmageed, W.; Wu, Y.; Rawls, S.; Harel, S.; Hassner, T.; Masi, I.; Choi, J.; Lekust, J.; Kim, J.; and Natarajan, P. In Applications of Computer Vision (WACV), 2016 IEEE Winter Conference on, pages 1–9, 2016. IEEE
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Computationally efficient template-based face recognition. Wu, Y.; AbdAlmageed, W.; Rawls, S.; and Natarajan, P. In Pattern Recognition (ICPR), 2016 23rd International Conference on, pages 1424–1429, 2016. IEEE
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Learning document image binarization from data. Wu, Y.; Natarajan, P.; Rawls, S.; and AbdAlmageed, W. In Image Processing (ICIP), 2016 IEEE International Conference on, pages 3763–3767, 2016. IEEE
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SemEval-2016 Task 8: Meaning Representation Parsing. May, J. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 1063–1073, San Diego, California, June 2016. Association for Computational Linguistics
SemEval-2016 Task 8: Meaning Representation Parsing [link]Paper   link   bibtex  
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies. Zoph, B.; Vaswani, A.; May, J.; and Knight, K. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1217–1222, San Diego, California, June 2016. Association for Computational Linguistics
Simple, Fast Noise-Contrastive Estimation for Large RNN Vocabularies [link]Paper   link   bibtex  
Transfer Learning for Low-Resource Neural Machine Translation. Zoph, B.; Yuret, D.; May, J.; and Knight, K. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1568–1575, Austin, Texas, November 2016. Association for Computational Linguistics
Transfer Learning for Low-Resource Neural Machine Translation [link]Paper   link   bibtex  
Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks. Zhu, L.; Guo, D.; Yin, J.; Steeg, G. V.; and Galstyan, A. IEEE Transactions on Knowledge and Data Engineering, 28(10): 2765–2777. October 2016.
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Emergence of Leadership in Communication. Allahverdyan, A. E.; and Galstyan, A. PLOS One, accepted. 2016.
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The DARPA Twitter Bot Challenge. Subrahmanian, V. S.; Azaria, A.; Durst, S.; Kagan, V.; Galstyan, A.; Lerman, K.; Zhu, L.; Ferrara, E.; Flammini, A.; Menczer, F.; Waltzman, R.; Stevens, A.; Dekhtyar, A.; Gao, S.; Hogg, T.; Kooti, F.; Liu, Y.; Varol, O.; Shiralkar, P.; Vydiswaran, V. G. V.; Mei, Q.; and Huang, T. IEEE Computer Magazine, 49(6): 38–46. June 2016.
The DARPA Twitter Bot Challenge [link]Paper   link   bibtex  
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text. Garg, S.; Galstyan, A.; Hermjakob, U.; and Marcu, D. In AAAI'16, 2016.
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Modeling Concept Dependencies in a Scientific Corpus. Gordon, J.; Zhu, L.; Galstyan, A.; Natarajan, P.; and Burns, G. In ACL'16, 2016.
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The Information Sieve. Steeg, G. V.; and Galstyan, A. In ICML'16, 2016.
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Latent Space Models for Multimodal Social Data. Cho, Y. S.; Ferrara, E.; Steeg, G. V.; and Galstyan, A. In WWW'16, 2016.
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Predicting online extremism, content adopters, and interaction reciprocity. Ferrara, E.; Wang, W.; Varol, O.; Flammini, A.; and Galstyan, A. In Social Informatics, 2016. Springer International Publishing
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Using Social Media, Online Social Networks, and Internet Search as Platforms for Public Health Interventions: A Pilot Study. Huesch, M. D.; Galstyan, A.; Ong, M. K.; and Doctor, J. N. Health Services Research, 51: 1273–1290. 2016.
Using Social Media, Online Social Networks, and Internet Search as Platforms for Public Health Interventions: A Pilot Study [link]Paper   doi   link   bibtex  
Toward Interpretable Topic Discovery via Anchored Correlation Explanation. Steeg, G. V.; Gao, S.; Reing, K.; and Galstyan, A. In ICML Workshop on Human Interpretability in Machine Learning (WHI'16), 2016.
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Variational Information Maximization for Feature Selection. Gao, S.; Steeg, G. V.; and Galstyan, A. In Advances in Neural Information Processing Systems, NIPS'16, 2016.
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Unsupervised Entity Resolution on Multi-type Graphs. Zhu, L.; Ghasemi-Gol, M.; Szekely, P.; Galstyan, A.; and Knoblock, C. A. In Groth, P.; Simperl, E.; Gray, A.; Sabou, M.; Krötzsch, M.; Lecue, F.; Flöck, F.; and Gil, Y., editor(s), The Semantic Web – ISWC 2016, pages 649–667, Cham, 2016. Springer International Publishing
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  2015 (13)
A Graphical Model Approach for Matching Partial Signatures. Du, X.; Doermann, D.; and Abd-Almageed, W. In IEEE Computer Vision and Pattern Recognition (CVPR), pages 1465–1472, Boston, MA, 2015.
A Graphical Model Approach for Matching Partial Signatures [pdf]Paper   link   bibtex  
Feature Selection using Partial Least Squares regression and optimal experiment design. Nagaraja, V.; and Abd-almageed, W. In 2015 International Joint Conference on Neural Networks IJCNN, pages 1–8, 2015.
Feature Selection using Partial Least Squares regression and optimal experiment design [link]Paper   doi   link   bibtex   abstract  
A Corpus of Rich Metaphor Annotation. Gordon, J.; Hobbs, J.; May, J.; Mohler, M.; Morbini, F.; Rink, B.; Tomlinson, M.; and Wertheim, S. In Proceedings of the Third Workshop on Metaphor in NLP, pages 56–66, Denver, Colorado, June 2015. Association for Computational Linguistics
A Corpus of Rich Metaphor Annotation [link]Paper   link   bibtex  
High-Precision Abductive Mapping of Multilingual Metaphors. Gordon, J.; Hobbs, J.; May, J.; and Morbini, F. In Proceedings of the Third Workshop on Metaphor in NLP, pages 50–55, Denver, Colorado, June 2015. Association for Computational Linguistics
High-Precision Abductive Mapping of Multilingual Metaphors [link]Paper   link   bibtex  
Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation. Pust, M.; Hermjakob, U.; Knight, K.; Marcu, D.; and May, J. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1143–1154, Lisbon, Portugal, September 2015. Association for Computational Linguistics
Parsing English into Abstract Meaning Representation Using Syntax-Based Machine Translation [link]Paper   link   bibtex  
Active Inference for Binary Symmetric Hidden Markov Models. Allahverdyan, A.; and Galstyan, A. Journal of Statistical Physics, 161(2): 452–466. 2015.
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Using Online Social Media and Social Networks as a Public Health Intervention. Huesch, M.; Doctor, J. N.; and Galstyan, A. In CESR-Schaeffer Working Paper No. 2015-011, 2015.
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Understanding Confounding Effects in Linguistic Coordination: An Information-Theoretic Approach. Gao, S.; Ver Steeg, G.; and Galstyan, A. PLoS ONE, 10(6): e0130167. 2015.
Understanding Confounding Effects in Linguistic Coordination: An Information-Theoretic Approach [link]Paper   doi   link   bibtex  
Efficient Estimation of Mutual Information for Strongly Dependent Variables. Gao, S.; Steeg, G. V.; and Galstyan, A. In AISTATS'15, 2015.
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Information-Theoretic Characterization of Blood Panel Predictors for Brain Atrophy and Cognitive Decline in the Elderly. Madsen, S. K.; Steeg, G. V.; Mezher, A.; Jahanshad, N.; Nir, T. M.; Hua, X.; Gutman, B. A.; Galstyan, A.; and Thompson, P. M. In International Symposium on Biomedical Imaging, ISBI, 2015.
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Maximally Informative Hierarchical Representations of High-Dimensional Data. Steeg, G. V.; and Galstyan, A. In AISTATS'15, 2015.
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Memory-induced mechanism for self-sustaining activity in networks. Allahverdyan, A. E.; Steeg, G. V.; and Galstyan, A. Phys. Rev. E, 92(6): 062824. December 2015.
Memory-induced mechanism for self-sustaining activity in networks [link]Paper   doi   link   bibtex  
Estimating Mutual Information by Local Gaussian Approximation. Gao, S.; Steeg, G. V.; and Galstyan, A. In Proc. of 31st Conference on Uncertainty in Artificial Intelligence (UAI'15), 2015.
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  2014 (11)
A Bayesian Nonparametric Approach to Integrative Genomics for Cancer Subgroup Discovery. Ozdemir, B.; Abd-Almageed, W.; Roessler, S.; and Wang, X. NIPS Workshop on Machine Learning for Computational Biology. 2014.
A Bayesian Nonparametric Approach to Integrative Genomics for Cancer Subgroup Discovery [pdf]Paper   link   bibtex  
An Arabizi-English Social Media Statistical Machine Translation System. May, J.; Benjira, Y.; and Echihabi, A. In Proceedings of the Eleventh Biennial Conference of the Association for Machine Translation in the Americas, Vancouver, Canada, October 2014. Association for Machine Translation in the Americas
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Demystifying Information-Theoretic Clustering. Steeg, G. V.; Galstyan, A.; Sha, F.; and DeDeo, S. In Proc. of ICML'14, 2014.
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Modeling Temporal Activity Patterns in Dynamic Social Networks. Raghavan, V.; Steeg, G. V.; Galstyan, A.; and Tartakovsky, A. G. IEEE Transactions on Computational Social Systems, 1(1): 89–107. March 2014.
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Phase Transitions in Community Detection: A Solvable Toy Model. Steeg, G. V.; Moore, C.; Galstyan, A.; and Allahverdyan, A. E. EPL (Europhysics Letters), 106(4): 48004. 2014.
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Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media. Zhu, L.; Galstyan, A.; Cheng, J.; and Lerman, K. In SIGMOD'14, 2014.
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Where and Why Users “Check In". Cho, Y. S.; Steeg, G. V.; and Galstyan, A. In AAAI'14, 2014.
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Discovering Structure in High-Dimensional Data Through Correlation Explanation. Steeg, G. V.; and Galstyan, A. In Advances in Neural Information Processing Systems, NIPS'14, 2014.
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Latent self-exciting point process model for spatial-temporal networks. Cho, Y.; Galstyan, A.; Brantingham, P. J.; and Tita, G. Discrete and Continuous Dynamical Systems - Series B, 19(5): 1335–1354. 2014.
Latent self-exciting point process model for spatial-temporal networks [link]Paper   doi   link   bibtex  
Opinion Dynamics with Confirmation Bias. Allahverdyan, A.; and Galstyan, A. PLoS ONE, 9(7): e99557. 2014.
Opinion Dynamics with Confirmation Bias [link]Paper   doi   link   bibtex  
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization. Zhu, L.; Steeg, G. V.; and Galstyan, A. In preprint, 2014.
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  2013 (5)
Models of Translation Competitions. Hopkins, M.; and May, J. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1416–1424, Sofia, Bulgaria, August 2013. Association for Computational Linguistics
Models of Translation Competitions [link]Paper   link   bibtex  
Statistical Tests for Contagion in Observational Social Network Studies. Steeg, G. V.; and Galstyan, A. In AISTATS'13, 2013.
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Sentiment Prediction using Collaborative Filtering. Kim, J.; Yoo, J.; Lim, H.; Qiu, H.; Kozareva, Z.; and Galstyan, A. In ICWSM'13, 2013.
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Coevolutionary networks of reinforcement-learning agents. Kianercy, A.; and Galstyan, A. Phys. Rev. E, 88(1): 012815. July 2013.
Coevolutionary networks of reinforcement-learning agents [link]Paper   doi   link   bibtex  
Explaining Away Stylistic Coordination. Gao, S.; Steeg, G. V.; and Galstyan, A. In WIN Workshop, New York, 2013.
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  2012 (6)
Dynamics of Boltzmann Q learning in two-player two-action games. Kianercy, A.; and Galstyan, A. Phys. Rev. E, 85(4): 041145. April 2012.
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Adaptive Agents on Evolving Networks. Kianercy, A.; Galstyan, A.; and Allahverdyan, A. In AAMAS-2012, 2012.
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Information Transfer in Social Media. Steeg, G. V.; and Galstyan, A. In WWW'12, 2012.
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Hidden Markov Models for the Activity Profile of Terrorist Groups. Raghavan, V.; Galstyan, A.; and Tartakovsky, A. G. submitted to Annals of Applied Statistics. 2012.
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Generative Models for Spatial-Temporal Processes with Applications to Predictive Criminology. Cho, Y. S.; Galstyan, A.; Brantingham, J.; and Tita, G. In 9th Bayesian Modeling Applications, UAI'12, 2012.
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Information-Theoretic Measures of Influence Based on Content Dynamics. Steeg, G. V.; and Galstyan, A. In in Proc. of WSDM'13, Rome, Italy, 2012.
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  2011 (7)
Statistical mechanics of semi-supervised clustering in sparse graphs. Steeg, G. V.; Galstyan, A.; and Allahverdyan, A. E. Journal of Statistical Mechanics: Theory and Experiment, 2011(08): P08009. 2011.
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Co-evolution of Selection and Influence in Social Networks. Cho, Y. S.; Steeg, G. V.; and Galstyan, A. In Proc. of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), 2011.
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Social Mechanics: An Empirically Grounded Science of Social Media. Lerman, K.; Hogg, T.; Galstyan, A.; and Steeg, G. V. In The Future of Social Web workshop, ICWSM-11, 2011.
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A Sequence of Relaxations Constraining Hidden Variable Models. Steeg, G. V.; and Galstyan, A. In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011); Best paper runner up, 2011.
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Continuous Strategy Replicator Dynamics for Multi–Agent Learning. Galstyan, A. accepted for publication in JAAMAS. 2011.
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Comparative Analysis of Viterbi Training and ML Estimation for HMMs. Allahverdyan, A.; and Galstyan, A. In Advances in Neural Information Processing Systems (NIPS), 2011.
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Le Chatelier's principle in replicator dynamics. Allahverdyan, A. E.; and Galstyan, A. Phys. Rev. E, 84(4): 041117. October 2011.
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  2010 (6)
Re-structuring, Re-labeling, and Re-aligning for Syntax-Based Machine Translation. Wang, W.; May, J.; Knight, K.; and Marcu, D. Computational Linguistics, 36(2): 247–277. June 2010.
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Efficient Inference through Cascades of Weighted Tree Transducers. May, J.; Knight, K.; and Vogler, H. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1058–1066, Uppsala, Sweden, July 2010. Association for Computational Linguistics
Efficient Inference through Cascades of Weighted Tree Transducers [link]Paper   link   bibtex  
Replicator Dynamics of Co-Evolving Networks. Galstyan, A.; Kianercy, A.; and Allahverdyan, A. In AAAI Fall Symposium on Complex Adaptive Systems, November 2010.
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Towards Modeling Social and Content Dynamics in Discussion Forums. Kim, J.; and Aram Galstyan, A. In NAACL Workshop on Computational Linguistics in a World of Social Media, June 2010.
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Community detection with and without prior information. Allahverdyan, A.; Steeg, G. V.; and Galstyan, A. EPL (Europhysics Letters), 90(1): 18002. 2010.
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Co-Evolving Mixed Membership BlockModels. Cho, Y. S.; Steeg, G. V.; and Galstyan, A. In NIPS workshopon Networks Across Disciplines, 2010.
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  2009 (4)
TENTACLES: Self-configuring robotic radio networks in unknown environments. Chiu, H. C. H.; Ryu, B.; Zhu, H.; Szekely, P. A.; Maheswaran, R. T.; Rogers, C. M.; Galstyan, A.; Salemi, B.; Rubenstein, M.; and Shen, W. In IROS, pages 1383–1388, 2009.
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Clustering with prior information. Allahverdyan, A.; Galstyan, A.; and Steeg, G. V. In NIPS workshop: Clustering: Science or Art? Towards Principled Approaches, 2009.
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On Maximum a Posteriori Estimation of Hidden Markov Processes. Allahverdyan, A.; and Galstyan, A. In Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence, June 2009.
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Maximizing influence propagation in networks with community structure. Galstyan, A.; Musoyan, V.; and Cohen, P. Phys. Rev. E, 79(5): 056102. May 2009.
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  2008 (5)
Training Tree Transducers. Graehl, J.; Knight, K.; and May, J. Computational Linguistics, 34(3): 391–427. September 2008.
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Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems. Crespi, V.; Galstyan, A.; and Lerman, K. Autonomous Robots, 24(3): 303–313. April 2008.
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Analysis of Social Voting Patterns on Digg. Lerman, K.; and Galstyan, A. In Proceedings of the 1st ACM SIGCOMM Workshop on Online Social Networks, 2008.
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Comparing Diffusion Models for Graph–Based Semi–Supervised Learning. Galstyan, A.; and Cohen, P. R. In 6-th International Workshop on Mining and Learning with Graphs, 2008.
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Influence Propagation in Modular Networks. Galstyan, A.; and Cohen, P. R. In AAAI Symposium on Social Information Processing, 2008.
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  2007 (5)
Bisimulation Minimisation for Weighted Tree Automata. Högberg, J.; Maletti, A.; and May, J. In Harju, T.; Karhumäki, J.; and Lepistö, A., editor(s), Proceedings of the 11th International Conference on Developments in Language Theory, DLT 2007, volume 4588, of Lecture Notes in Computer Science, pages 229–240, Turku, Finland, July 2007. Springer-Verlag
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Syntactic Re-Alignment Models for Machine Translation. May, J.; and Knight, K. In Eisner, J.; and Kudo, T., editor(s), Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 360–368, Prague, Czech Republic, June 2007. Association for Computational Linguistics
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Backward and Forward Bisimulation Minimisation of Weighted Tree Automata. Högberg, J.; Maletti, A.; and May, J. In Holub, J.; and Ždárek, J., editor(s), Proceedings of the 12th International Conference on Implementation and Application of Automata, CIAA 2007, volume 4783, of Lecture Notes in Computer Science, pages 109–121, Prague, Czech Republic, July 2007. Springer-Verlag
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Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification. Galstyan, A.; and Cohen, P. R. In Proceedings of 17th International Conference Inductive Logic Programming, pages 98–111, 2007.
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Cascading dynamics in modular networks. Galstyan, A.; and Cohen, P. Phys. Rev. E, 75(3): 036109. March 2007.
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  2006 (4)
A Better N-Best List: Practical Determinization of Weighted Finite Tree Automata. May, J.; and Knight, K. In Khudanpur, S.; and Roark, B., editor(s), Proceedings of the 2006 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, volume Main Proceedings, pages 351–358, Brooklyn, New York, June 2006. Association for Computational Linguistics
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Tiburon: A Weighted Tree Automata Toolkit. May, J.; and Knight, K. In Ibarra, O. H.; and Yen, H., editor(s), Proceedings of the 11th International Conference of Implementation and Application of Automata, CIAA 2006, volume 4094, of Lecture Notes in Computer Science, pages 102–113, Taipei, Taiwan, August 2006. Springer
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Analysis of Dynamic Task Allocation in Multi-Robot Systems. Lerman, K.; Jones, C. V.; Galstyan, A.; and Matarić, M. J. International Journal of Robotics Research, 25(3): 225–242. 2006.
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Relational Classification Through Three–State Epidemic Dynamics. Galstyan, A.; and Cohen, P. R. In The 9th International Conference on Information Fusion, Florence, Italy, 2006.
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  2005 (8)
Analysis of a Stochastic Model of Adaptive Task Allocation in Robots. Galstyan, A.; and Lerman, K. In Breuckner, S.; Serugendo, G. D. M.; Karageorgos, A.; and Nagpal, R., editor(s), Engineering Self Organizing Systems: Methodology and Applicationsy, of LNAI, pages 167, Berlin Heidelberg, 2005. Springer-Verlag
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A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems. Lerman, K.; Martinoli, A.; and Galstyan, A. In E, S.; and W, S., editor(s), Swarm Robotics Workshop: State-of-the-art Survey, of LNCS, pages 143–152. Springer-Verlag, Berlin Heidelberg, 2005.
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Comparative Analysis of Top-down and Bottom-up Methodologies for Multi-Agent Systems. Crespi, V.; Galstyan, A.; and Lerman, K. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2005) (poster), Utrecht, Netherlands, June 2005.
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Resource Allocation in the Grid with Learning Agents. Galstyan, A.; Czajkowski, K.; and Lerman, K. Journal of Grid Computing, 3(1–2): 91–100. June 2005.
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Modeling and mathematical analysis of swarms of microscopic robots. Galstyan, A.; Hogg, T.; and Lerman, K. In Proceedings of IEEE Swarm Intelligence Symposium (SIS-2005), Pasadena, CA, June 2005.
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A Systematic Approach to Composing and Optimizing Application Workflows. Deelman, E.; Galstyan, A.; Gil, Y.; Hall, M.; Lerman, K.; Nakano, A.; Vashista, P.; and Saltz, J. In Proceedings of Workshop on Patterns in High Performance Computing, 2005.
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Identifying Covert Sub-Networks Through Iterative Node Classification. Galstyan, A.; and Cohen, P. R. In Proceedings of First International Conference on Intelligence Analysis, McLean, VA, 2005.
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Inferring Useful Heuristics from the Dynamics of Iterative Relational Classifiers. Galstyan, A.; and Cohen, P. R. In Proceedings of IJCAI-05, 19th International Joint Conference on Artificial Intelligence, 2005.
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  2004 (6)
Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms. Shen, W.; Will, P.; Galstyan, A.; and Chuong, C. Auton. Robots, 17(1): 93–105. 2004.
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Automatically Modeling Group Behavior of Simple Agents. Lerman, K.; and Galstyan, A. In Agent Modeling Workshop, AAMAS-04. 2004.
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Two Paradigms for the Design of Artificial Collectives. Lerman, K.; and Galstyan, A. In Tumer, K.; and Wolpert, D., editor(s), Collectives and Design of Complex Systems, pages 231–256. Springer Verlag, 2004.
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Resource Allocation and Emergent Coordination in Wireless Sensor Networks. Galstyan, A.; Krishnamachari, B.; and Lerman, K. In Workshop on Sensor Networks at the The 19th National Conference on Artificial Intelligence (AAAI-04), 2004.
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Distributed Online Localization in Sensor Networks Using a Moving Target. Galstyan, A.; Krishnamachari, B.; Pattem, S.; and Lerman, K. In Information Processing in Sensor Networks (IPSN-2004), Berkeley, CA, April 2004.
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Resource Allocation in the Grid Using Reinforcement Learning. Galstyan, A.; Czajkowski, K.; and Lerman, K. In Proceedings of International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2004) (poster), New York, NY, pages 1314–1315, July 2004.
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  2003 (7)
Answer Selection and Confidence Estimation. Xu, J.; Licuanan, A.; May, J.; Miller, S.; and Weischedel, R. M. In New Directions in Question Answering, pages 134–137, 2003.
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New Directions in Question Answering, Papers from 2003 AAAI Spring Symposium, Stanford University, Stanford, CA, USA. Maybury, M. T., editor. AAAI Press, 2003.
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Resource Allocation Games with Changing Resource Capacities. Galstyan, A.; Kolar, S.; and Lerman, K. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), Melbourne, Australia, pages 145–152, 2003.
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A Methodology for Mathematical Analysis of Multi-Agent Systems. Lerman, K.; Galstyan, A.; and Hogg, T. unpublished. 2003.
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Agent Memory and Adaptation in Multi-Agent Systems. Lerman, K.; and Galstyan, A. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-2003), Melbourne, Australia, pages 797–803, July 2003.
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Threshold Behavior in Boolean Network Model for SAT. Bugacov, A.; Galstyan, A.; and Lerman, K. In Proceedings of the International Conference on Artificial Intelligence (IC-AI-2003), Las Vegas, NV, pages 87–92, June 2003.
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Macroscopic Analysis of Adaptive Task Allocation in Robots. Lerman, K.; and Galstyan, A. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS-2003), Las Vegas, NV, pages 1951–1956, October 2003.
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  2002 (4)
TREC 2002 QA at BBN: Answer Selection and Confidence Estimation. Xu, J.; Licuanan, A.; May, J.; Miller, S.; and Weischedel, R. M. In TREC, 2002.
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Minority Games and Distributed Coordination in Non-Stationary Environments. Galstyan, A.; and Lerman, K. In Proc. of Int. Joint Conf. on Neural Networks, 2002.
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Mathematical Model of Foraging in a Group of Robots: Effect of Interference. Lerman, K.; and Galstyan, A. Autonomous Robots, 13(2): 127–141. 2002.
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Adaptive Boolean Networks and Minority Games with Time-Dependent Cutoffs. Galstyan, A.; and Lerman, K. Physical Review E, 66: 015103. 2002.
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  2001 (2)
A General Methodology for Mathematical Analysis of Multi-Agent Systems. Lerman, K.; and Galstyan, A. Technical Report ISI-TR-529, University of California, Information Sciences Institute, 2001.
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A Macroscopic Analytical Model of Collaboration in Distributed Robotic Systems. Lerman, K.; Galstyan, A.; Martinoli, A.; and Ijspeert, A. Artificial Life Journal, 7(4): 375–393. 2001.
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An Analysis (and an Annotated Corpus) of User Responses to Machine Translation Output. Pighin, D.; Màrquez, L.; and May, J. In Chair), N. C. (.; Choukri, K.; Declerck, T.; Doğan, M. U.; Maegaard, B.; Mariani, J.; Odijk, J.; and Piperidis, S., editor(s), Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12), Istanbul, Turkey, . European Language Resources Association (ELRA)
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