Publications

BibBase galstyan, a
generated by bibbase.org
  2022 (8)
Failure modes of domain generalization algorithms. Galstyan, T.; Harutyunyan, H.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19077–19086, 2022.
link   bibtex  
A Metric Space for Point Process Excitations. Marmarelis, M. G; Ver Steeg, G.; and Galstyan, A. Journal of Artificial Intelligence Research, 73: 1323–1353. 2022.
link   bibtex  
Inferring topological transitions in pattern-forming processes with self-supervised learning. Abram, M.; Burghardt, K.; Ver Steeg, G.; Galstyan, A.; and Dingreville, R. npj Computational Materials, 8(1): 205. 2022.
link   bibtex  
Robust Conversational Agents against Imperceptible Toxicity Triggers. Mehrabi, N.; Beirami, A.; Morstatter, F.; and Galstyan, A. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022.
link   bibtex  
Formal limitations of sample-wise information-theoretic generalization bounds. Harutyunyan, H.; Ver Steeg, G.; and Galstyan, A. In 2022 IEEE Information Theory Workshop (ITW), pages 440–445, 2022. IEEE
link   bibtex  
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations. Ghazarian, S.; Wen, N.; Galstyan, A.; and Peng, N. In ACL 2022, 2022.
link   bibtex  
StATIK: Structure and text for inductive knowledge graph completion. Markowitz, E.; Balasubramanian, K.; Mirtaheri, M.; Annavaram, M.; Galstyan, A.; and Ver Steeg, G. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 604–615, 2022.
link   bibtex  
Identifying geopolitical event precursors using attention-based LSTMs. Hossain, K. T.; Harutyunyan, H.; Ning, Y.; Kennedy, B.; Ramakrishnan, N.; and Galstyan, A. Frontiers in Artificial Intelligence, 5. 2022.
link   bibtex  
  2021 (26)
Identifying and analyzing cryptocurrency manipulations in social media. Mirtaheri, M.; Abu-El-Haija, S.; Morstatter, F.; Ver Steeg, G.; and Galstyan, A. IEEE Transactions on Computational Social Systems, 8(3): 607–617. 2021.
link   bibtex  
A survey on bias and fairness in machine learning. Mehrabi, N.; Morstatter, F.; Saxena, N.; Lerman, K.; and Galstyan, A. ACM Computing Surveys (CSUR), 54(6): 1–35. 2021.
link   bibtex   1 download  
NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding. Wang, K.; Stevens, R.; Alachram, H.; Li, Y.; Soldatova, L.; King, R.; Ananiadou, S.; Schoene, A. M; Li, M.; Christopoulou, F.; and others NPJ systems biology and applications, 7(1): 1–8. 2021.
link   bibtex  
Muscle: strengthening semi-supervised learning via concurrent unsupervised learning using mutual information maximization. Xie, H.; Hussein, M. E; Galstyan, A.; and Abd-Almageed, W. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2586–2595, 2021.
link   bibtex  
Discovering Higher-Order Interactions Through Neural Information Decomposition. Reing, K.; Ver Steeg, G.; and Galstyan, A. Entropy, 23(1): 79. 2021.
link   bibtex   4 downloads  
Discol: Toward engaging dialogue systems through conversational line guided response generation. Ghazarian, S.; Liu, Z.; Chakrabarty, T.; Ma, X.; Galstyan, A.; and Peng, N. In NAACL-HLT'21 (Demo Track), 2021.
link   bibtex  
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. Markowitz, E.; Balasubramanian, K.; Mirtaheri, M.; Abu-El-Haija, S.; Perozzi, B.; Steeg, G. V.; and Galstyan, A. In ICLR, 2021.
link   bibtex  
A survey of human judgement and quantitative forecasting methods. Zellner, M.; Abbas, A. E; Budescu, D. V; and Galstyan, A. Royal Society open science, 8(2): 201187. 2021.
link   bibtex  
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources. Mehrabi, N.; Zhou, P.; Morstatter, F.; Pujara, J.; Ren, X.; and Galstyan, A. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021.
link   bibtex  
Influence Decompositions For Neural Network Attribution. Reing, K.; Ver Steeg, G.; and Galstyan, A. In International Conference on Artificial Intelligence and Statistics, pages 2710–2718, 2021. PMLR
link   bibtex  
Identifying botnet IP address clusters using natural language processing techniques on honeypot command logs. Crespi, V.; Hardaker, W.; Abu-El-Haija, S.; and Galstyan, A. arXiv preprint arXiv:2104.10232. 2021.
link   bibtex  
Bin2vec: learning representations of binary executable programs for security tasks. Arakelyan, S.; Arasteh, S.; Hauser, C.; Kline, E.; and Galstyan, A. Cybersecurity, 4(1): 1–14. 2021.
link   bibtex  
q-Paths: Generalizing the geometric annealing path using power means. Masrani, V.; Brekelmans, R.; Bui, T.; Nielsen, F.; Galstyan, A.; Ver Steeg, G.; and Wood, F. In Uncertainty in Artificial Intelligence, pages 1938–1947, 2021. PMLR
link   bibtex  
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2107.01598. 2021.
link   bibtex  
Attributing Fair Decisions with Attention Interventions. Mehrabi, N.; Gupta, U.; Morstatter, F.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2109.03952. 2021.
link   bibtex  
Partner-assisted learning for few-shot image classification. Ma, J.; Xie, H.; Han, G.; Chang, S.; Galstyan, A.; and Abd-Almageed, W. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10573–10582, 2021.
link   bibtex  
Information-theoretic generalization bounds for black-box learning algorithms. Harutyunyan, H.; Raginsky, M.; Ver Steeg, G.; and Galstyan, A. Advances in Neural Information Processing Systems, 34. 2021.
link   bibtex  
Cognitively Inspired Learning of Incremental Drifting Concepts. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2110.04662. 2021.
link   bibtex  
Implicit SVD for Graph Representation Learning. Abu-El-Haija, S.; Mostafa, H.; Nassar, M.; Crespi, V.; Ver Steeg, G.; and Galstyan, A. Advances in Neural Information Processing Systems, 34. 2021.
link   bibtex  
Failure Modes of Domain Generalization Algorithms. Galstyan, T.; Harutyunyan, H.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2111.13733. 2021.
link   bibtex  
Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling. Ver Steeg, G.; and Galstyan, A. In Thirty-Fifth Conference on Neural Information Processing Systems, 2021.
link   bibtex  
Layer-Wise Neural Network Compression via Layer Fusion. O’Neill, J.; Steeg, G. V; and Galstyan, A. In Asian Conference on Machine Learning, pages 1381–1396, 2021. PMLR
link   bibtex  
Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation. Ghazarian, S.; Liu, Z.; SM, A.; Weischedel, R.; Galstyan, A.; and Peng, N. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021.
link   bibtex  
Exacerbating Algorithmic Bias through Fairness Attacks. Mehrabi, N.; Naveed, M.; Morstatter, F.; and Galstyan, A. In AAAI'21, 2021.
link   bibtex  
One-shot learning for temporal knowledge graphs. Mirtaheri, M.; Rostami, M.; Ren, X.; Morstatter, F.; and Galstyan, A. In Automayed Knowledge Base Construction, AKBC 2021, 2021.
link   bibtex  
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data. Jin, W.; Khanna, R.; Kim, S.; Lee, D.; Morstatter, F.; Galstyan, A.; and Ren, X. In ACL 2021, 2021.
link   bibtex  
  2020 (19)
Forecasting violent events in the middle East and North Africa using the hidden Markov model and regularized autoregressive models. Hossain, K. T.; Gao, S.; Kennedy, B.; Galstyan, A.; and Natarajan, P. The Journal of Defense Modeling and Simulation, 17(3): 269–283. 2020.
link   bibtex  
Stacking models for nearly optimal link prediction in complex networks. Ghasemian, A.; Hosseinmardi, H.; Galstyan, A.; Airoldi, E. M; and Clauset, A. Proceedings of the National Academy of Sciences, 117(38): 23393–23400. 2020.
link   bibtex  
Man is to person as woman is to location: Measuring gender bias in named entity recognition. Mehrabi, N.; Gowda, T.; Morstatter, F.; Peng, N.; and Galstyan, A. In Proceedings of the 31st ACM Conference on Hypertext and Social Media, pages 231–232, 2020.
link   bibtex  
Predictive engagement: An efficient metric for automatic evaluation of open-domain dialogue systems. Ghazarian, S.; Weischedel, R.; Galstyan, A.; and Peng, N. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 7789–7796, 2020.
link   bibtex  
Improving generalization by controlling label-noise information in neural network weights. Harutyunyan, H.; Reing, K.; Ver Steeg, G.; and Galstyan, A. In International Conference on Machine Learning, pages 4071–4081, 2020. PMLR
link   bibtex  
Anchor Attention for Hybrid Crowd Forecasts Aggregation. Huang, Y.; Abeliuk, A.; Morstatter, F.; Atanasov, P.; and Galstyan, A. arXiv preprint arXiv:2003.12447. 2020.
link   bibtex  
Graph embedding with personalized context distribution. Huang, D.; He, Z.; Huang, Y.; Sun, K.; Abu-El-Haija, S.; Perozzi, B.; Lerman, K.; Morstatter, F.; and Galstyan, A. In Companion Proceedings of the Web Conference 2020, pages 655–661, 2020.
link   bibtex  
Modeling Dialogues with Hashcode Representations: A Nonparametric Approach. Garg, S.; Rish, I.; Cecchi, G.; Goyal, P.; Ghazarian, S.; Gao, S.; Ver Steeg, G.; and Galstyan, A. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 3970–3979, 2020.
link   bibtex  
All in the exponential family: Bregman duality in thermodynamic variational inference. Brekelmans, R.; Masrani, V.; Wood, F.; Steeg, G. V.; and Galstyan, A. In ICML 2020, 2020.
link   bibtex   3 downloads  
Sequential unsupervised domain adaptation through prototypical distributions. Rostami, M.; and Galstyan, A. . 2020.
link   bibtex  
Robust Classification under Class-Dependent Domain Shift. Galstyan, T.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2007.05335. 2020.
link   bibtex  
Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms. Matsui, A.; Ferrara, E.; Morstatter, F.; Abeliuk, A.; and Galstyan, A. arXiv preprint arXiv:2009.01966. 2020.
link   bibtex  
Quantifying machine influence over human forecasters. Abeliuk, A.; Benjamin, D. M; Morstatter, F.; and Galstyan, A. Scientific reports, 10(1): 1–14. 2020.
link   bibtex  
Likelihood ratio exponential families. Brekelmans, R.; Nielsen, F.; Makhzani, A.; Galstyan, A.; and Steeg, G. V. arXiv preprint arXiv:2012.15480. 2020.
link   bibtex   3 downloads  
Annealed importance sampling with q-paths. Brekelmans, R.; Masrani, V.; Bui, T.; Wood, F.; Galstyan, A.; Steeg, G. V.; and Nielsen, F. arXiv preprint arXiv:2012.07823. 2020.
link   bibtex   6 downloads  
Latent Embeddings of Point Process Excitations. Marmarelis, M. G; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2005.02515. 2020.
link   bibtex  
Domain Agnostic Prototypical Distribution for Unsupervised Model Adaptation. Rostami, M.; and Galstyan, A. . 2020.
link   bibtex  
Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis. Rostami, M.; and Galstyan, A. . 2020.
link   bibtex  
Sequential Model Adaptation Using Domain Agnostic Internal Distributions. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2007.00197. 2020.
link   bibtex  
  2019 (14)
Multitask learning and benchmarking with clinical time series data. Harutyunyan, H.; Khachatrian, H.; Kale, D. C; Ver Steeg, G.; and Galstyan, A. Scientific data, 6(1): 1–18. 2019.
link   bibtex  
Kernelized hashcode representations for relation extraction. Garg, S.; Galstyan, A.; Ver Steeg, G.; Rish, I.; Cecchi, G.; and Gao, S. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 6431–6440, 2019.
link   bibtex  
Auto-encoding total correlation explanation. Gao, S.; Brekelmans, R.; Ver Steeg, G.; and Galstyan, A. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 1157–1166, 2019. PMLR
link   bibtex  
Coupled clustering of time-series and networks. Liu, Y.; Zhu, L.; Szekely, P.; Galstyan, A.; and Koutra, D. In Proceedings of the 2019 SIAM International Conference on Data Mining, pages 531–539, 2019. Society for Industrial and Applied Mathematics
link   bibtex  
Debiasing community detection: The importance of lowly connected nodes. Mehrabi, N.; Morstatter, F.; Peng, N.; and Galstyan, A. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 509–512, 2019. IEEE
link   bibtex  
Exact rate-distortion in autoencoders via echo noise. Brekelmans, R.; Moyer, D.; Galstyan, A.; and Ver Steeg, G. In Advances in neural information processing systems, volume 32, 2019.
link   bibtex  
Better automatic evaluation of open-domain dialogue systems with contextualized embeddings. Ghazarian, S.; Wei, J. T.; Galstyan, A.; and Peng, N. arXiv preprint arXiv:1904.10635. 2019.
link   bibtex  
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. Abu-El-Haija, S.; Perozzi, B.; Kapoor, A.; Alipourfard, N.; Lerman, K.; Harutyunyan, H.; Ver Steeg, G.; and Galstyan, A. In International Conference on Machine Learning, pages 21–29, 2019. PMLR
link   bibtex  
Efficient covariance estimation from temporal data. Harutyunyan, H.; Moyer, D.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1905.13276. 2019.
link   bibtex  
Biorelex 1.0: Biological relation extraction benchmark. Khachatrian, H.; Nersisyan, L.; Hambardzumyan, K.; Galstyan, T.; Hakobyan, A.; Arakelyan, A.; Rzhetsky, A.; and Galstyan, A. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 176–190, 2019.
link   bibtex  
Fast structure learning with modular regularization. Ver Steeg, G.; Harutyunyan, H.; Moyer, D.; and Galstyan, A. Advances in Neural Information Processing Systems, 32. 2019.
link   bibtex  
Deep structured neural network for event temporal relation extraction. Han, R.; Hsu, I; Yang, M.; Galstyan, A.; Weischedel, R.; Peng, N.; and others In Proceedings of the 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2019.
link   bibtex  
SAGE: A Hybrid Geopolitical Event Forecasting System. Morstatter, F.; Galstyan, A.; Satyukov, G.; Benjamin, D.; Abeliuk, A.; Mirtaheri, M.; Hossain, K. T.; Szekely, P. A; Ferrara, E.; Matsui, A.; and others In IJCAI, volume 1, pages 6557–6559, 2019.
link   bibtex  
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction. Garg, S.; Galstyan, A.; Ver Steeg, G.; and Cecchi, G. A In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4026–4036, 2019.
link   bibtex  
  2018 (9)
Stochastic learning of nonstationary kernels for natural language modeling. Garg, S.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1801.03911. 2018.
link   bibtex  
From alt-right to alt-rechts: Twitter analysis of the 2017 german federal election. Morstatter, F.; Shao, Y.; Galstyan, A.; and Karunasekera, S. In Companion Proceedings of the The Web Conference 2018, pages 621–628, 2018.
link   bibtex  
Modeling psychotherapy dialogues with kernelized hashcode representations: A nonparametric information-theoretic approach. Garg, S.; Rish, I.; Cecchi, G.; Goyal, P.; Ghazarian, S.; Gao, S.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1804.10188. 2018.
link   bibtex  
Mining and forecasting career trajectories of music artists. Arakelyan, S.; Morstatter, F.; Martin, M.; Ferrara, E.; and Galstyan, A. In Proceedings of the 29th on Hypertext and Social Media, pages 11–19. 2018.
link   bibtex  
A Forest Mixture Bound for Block-Free Parallel Inference. Lawton, N.; Galstyan, A.; and Steeg, G. V. In Uncertainty in Artificial Intelligence (UAI), 2018.
link   bibtex  
Embedding networks with edge attributes. Goyal, P.; Hosseinmardi, H.; Ferrara, E.; and Galstyan, A. In Proceedings of the 29th on Hypertext and Social Media, pages 38–42. 2018.
link   bibtex  
Uncovering biologically coherent peripheral signatures of health and risk for Alzheimer’s disease in the aging brain. Riedel, B. C; Daianu, M.; Ver Steeg, G.; Mezher, A.; Salminen, L. E; Galstyan, A.; Thompson, P. M; Initiative, A. D. N.; and others Frontiers in aging neuroscience,390. 2018.
link   bibtex  
Dialogue Modeling Via Hash Functions. Garg, S.; Cecchi, G. A; Rish, I.; Gao, S.; Ver Steeg, G.; Ghazarian, S.; Goyal, P.; and Galstyan, A. In LaCATODA@ IJCAI, 2018.
link   bibtex  
Invariant representations without adversarial training. Moyer, D.; Gao, S.; Brekelmans, R.; Steeg, G. V.; and Galstyan, A. In Advances in Neural Information Processing Systems (NeurIPS), 2018.
link   bibtex  
  2017 (4)
Unifying Local and Global Change Detection in Dynamic Networks. Li, W.; Guo, D.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1710.03035. 2017.
link   bibtex  
Disentangled representations via synergy minimization. Ver Steeg, G.; Brekelmans, R.; Harutyunyan, H.; and Galstyan, A. In 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 180–187, 2017. IEEE
link   bibtex   1 download  
Highly Accurate Link Prediction in Networks Using Stacked Generalization. Ghasemian, A.; Galstyan, A.; and Clauset, A. . 2017.
link   bibtex  
Sifting Common Information from Many Variables. Ver Steeg, G.; Gao, S.; Reing, K.; and Galstyan, A. In IJCAI, pages 2885–2892, 2017.
link   bibtex  
  2016 (16)
The information sieve. Ver Steeg, G.; and Galstyan, A. In International Conference on Machine Learning, pages 164–172, 2016. PMLR
link   bibtex  
Latent space model for multi-modal social data. Cho, Y.; Ver Steeg, G.; Ferrara, E.; and Galstyan, A. In Proceedings of the 25th International Conference on World Wide Web, pages 447–458, 2016.
link   bibtex  
Extracting biomolecular interactions using semantic parsing of biomedical text. Garg, S.; Galstyan, A.; Hermjakob, U.; and Marcu, D. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.
link   bibtex  
The DARPA Twitter bot challenge. Subrahmanian, V. S; Azaria, A.; Durst, S.; Kagan, V.; Galstyan, A.; Lerman, K.; Zhu, L.; Ferrara, E.; Flammini, A.; and Menczer, F. Computer, 49(6): 38–46. 2016.
link   bibtex   8 downloads  
Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly. Madsen, S. K; Ver Steeg, G.; Daianu, M.; Mezher, A.; Jahanshad, N.; Nir, T. M; Hua, X.; Gutman, B. A; Galstyan, A.; and Thompson, P. M In Medical Imaging 2016: Image Processing, volume 9784, pages 978411, 2016. International Society for Optics and Photonics
link   bibtex  
Predicting online extremism, content adopters, and interaction reciprocity. Ferrara, E.; Wang, W.; Varol, O.; Flammini, A.; and Galstyan, A. In International conference on social informatics, pages 22–39, 2016. Springer, Cham
link   bibtex  
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.
link   bibtex  
Toward interpretable topic discovery via anchored correlation explanation. Reing, K.; Kale, D. C; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1606.07043. 2016.
link   bibtex  
Scalable temporal latent space inference for link prediction in dynamic social networks. Zhu, L.; Guo, D.; Yin, J.; Ver Steeg, G.; and Galstyan, A. IEEE Transactions on Knowledge and Data Engineering, 28(10): 2765–2777. 2016.
link   bibtex  
Emergence of leadership in communication. Allahverdyan, A. E; and Galstyan, A. PloS one, 11(8): e0159301. 2016.
link   bibtex  
Unsupervised entity resolution on multi-type graphs. Zhu, L.; Ghasemi-Gol, M.; Szekely, P.; Galstyan, A.; and Knoblock, C. A In International semantic web conference, pages 649–667, 2016. Springer, Cham
link   bibtex   29 downloads  
Sifting common information from many variables. Steeg, G. V.; Gao, S.; Reing, K.; and Galstyan, A. arXiv preprint arXiv:1606.02307. 2016.
link   bibtex  
Inferring Structure and Forecasting Dynamics on Evolving Networks. Brantingham, J. P; Breiger, R.; Chang, Y.; Galstyan, A.; Lerman, K.; McBride, M.; Mezic, I.; Milward, B.; Morrison, C.; Percus, A.; and others Technical Report UCLA Office of Contract and Grant Administration Los Angeles United States, 2016.
link   bibtex  
Variational information maximization for feature selection. Gao, S.; Ver Steeg, G.; and Galstyan, A. Advances in neural information processing systems, 29. 2016.
link   bibtex  
Situational Awareness for Social Media: Theories, Models and Algorithms. Galstyan, A.; Lerman, K.; Hovy, E.; Liu, Y.; and Nevatia, R. Technical Report University of Southern California Los Angeles United States, 2016.
link   bibtex  
Using Social Media, Online Social Networks and Internet Search As Platforms for Public Health Interventions. Huesch, M.; Galstyan, A.; Doctor, J.; and Ong, M. In 2016 Annual Research Meeting, 2016. AcademyHealth
link   bibtex  
  2015 (10)
Active inference for binary symmetric hidden Markov models. Allahverdyan, A. E; and Galstyan, A. Journal of Statistical Physics, 161(2): 452–466. 2015.
link   bibtex  
Learning bounded rationality models of the adversary in repeated Stackelberg Security Games. Kar, D.; Fang, F.; Delle Fave, F.; Sintov, N.; Sinha, A.; Galstyan, A.; An, B.; and Tambe, M. Retrieved from Nanyang Technological University: http://www3. ntu. edu. sg/home/boan/papers/ALA15_Debarun. pdf. 2015.
link   bibtex  
Predicting Cognitive Decline with Information-Theoretic Clustering of Brain MRI and Blood Tests. Madsen, S. K; Ver Steeg, G.; Mezher, A.; Jahanshad, N.; Nir, T. M; Hua, X.; Gutman, B. A; Galstyan, A.; and Thompson, P. M In BIOLOGICAL PSYCHIATRY, volume 77, pages 96S–96S, 2015. ELSEVIER SCIENCE INC
link   bibtex  
Information-theoretic characterization of blood panel predictors for brain atrophy and cognitive decline in the elderly. Madsen, S. K; Ver Steeg, G.; Mezher, A.; Jahanshad, N.; Nir, T. M; Hua, X.; Gutman, B. A; Galstyan, A.; and Thompson, P. M In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 980–984, 2015. IEEE
link   bibtex  
Using Online Social Media and Social Networks as a Public Health Intervention. Huesch, M.; Doctor, J. N; and Galstyan, A. CESR-Schaeffer Working Paper, (2015-011). 2015.
link   bibtex  
Estimating Mutual Information by Local Gaussian Approximation. Gao, S.; Steeg, G. V.; and Galstyan, A. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, of UAI'15, pages 278–287, Arlington, Virginia, USA, 2015. AUAI Press
link   bibtex  
Memory-induced mechanism for self-sustaining activity in networks. Allahverdyan, A. E; Ver Steeg, G.; and Galstyan, A. Physical Review E, 92(6): 062824. 2015.
link   bibtex  
Information-theoretic clustering of neuroimaging metrics related to cognitive decline in the elderly. Daianu, M.; Steeg, G. V.; Mezher, A.; Jahanshad, N.; Nir, T. M; Yan, X.; Prasad, G.; Lerman, K.; Galstyan, A.; and Thompson, P. M In International MICCAI Workshop on Medical Computer Vision, pages 13–23, 2015. Springer, Cham
link   bibtex  
Outreach Using Social Media Campaigns: Micro-Education for $1 per Patient. Huesch, M.; Doctor, J.; and Galstyan, A. In 2015 Annual Research Meeting, 2015. AcademyHealth
link   bibtex  
Efficient estimation of mutual information for strongly dependent variables. Gao, S.; Ver Steeg, G.; and Galstyan, A. In Artificial intelligence and statistics, pages 277–286, 2015. PMLR
link   bibtex  
  2014 (9)
Modeling temporal activity patterns in dynamic social networks. Raghavan, V.; Ver Steeg, G.; Galstyan, A.; and Tartakovsky, A. G IEEE Transactions on Computational Social Systems, 1(1): 89–107. 2014.
link   bibtex  
Phase transitions in community detection: A solvable toy model. Ver Steeg, G.; Moore, C.; Galstyan, A.; and Allahverdyan, A. EPL (Europhysics Letters), 106(4): 48004. 2014.
link   bibtex   22 downloads  
Tripartite graph clustering for dynamic sentiment analysis on social media. Zhu, L.; Galstyan, A.; Cheng, J.; and Lerman, K. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 1531–1542, 2014.
link   bibtex   41 downloads  
Where and why users “check in”. Cho, Y.; Ver Steeg, G.; and Galstyan, A. In Proc. of AAAI, volume 14, pages 269–275, 2014.
link   bibtex   40 downloads  
Discovering structure in high-dimensional data through correlation explanation. Ver Steeg, G.; and Galstyan, A. Advances in Neural Information Processing Systems, 27. 2014.
link   bibtex  
Opinion dynamics with confirmation bias. Allahverdyan, A. E; and Galstyan, A. PloS one, 9(7): e99557. 2014.
link   bibtex  
Mixed Membership Blockmodels for Dynamic Networks with Feedback. Cho, Y.; Ver Steeg, G.; and Galstyan, A. 2014.
link   bibtex  
Maximally Informative Hierarchical Representations of High-Dimensional Data. Ver Steeg, G.; and Galstyan, A. In Artificial Intelligence and Statistics (AISTATS), 2014.
link   bibtex  
Understanding confounding effects in linguistic coordination: an information-theoretic approach. Gao, S.; Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1412.0696. 2014.
link   bibtex  
  2013 (10)
Continuous strategy replicator dynamics for multi-agent q-learning. Galstyan, A. Autonomous agents and multi-agent systems, 26(1): 37–53. 2013.
link   bibtex  
Hidden Markov models for the activity profile of terrorist groups. Raghavan, V.; Galstyan, A.; and Tartakovsky, A. G The Annals of Applied Statistics,2402–2430. 2013.
link   bibtex  
Sentiment prediction using collaborative filtering. Kim, J.; Yoo, J.; Lim, H.; Qiu, H.; Kozareva, Z.; and Galstyan, A. In Proceedings of the International AAAI Conference on Web and Social Media, volume 7, pages 685–688, 2013.
link   bibtex   6 downloads  
Vaccination (anti-) campaigns in social media. Huesch, M.; Ver Steeg, G.; and Galstyan, A. In Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.
link   bibtex  
Socially relevant venue clustering from check-in data. Cho, Y.; Ver Steeg, G.; and Galstyan, A. In KDD Workshop on Mining and Learning with Graphs, 2013.
link   bibtex  
Coevolutionary networks of reinforcement-learning agents. Kianercy, A.; and Galstyan, A. Physical Review E, 88(1): 012815. 2013.
link   bibtex   6 downloads  
Coupled hidden markov models for user activity in social networks. Raghavan, V.; Ver Steeg, G.; Galstyan, A.; and Tartakovsky, A. G In 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pages 1–6, 2013. IEEE
link   bibtex  
Demystifying information-theoretic clustering. Ver Steeg, G.; Galstyan, A.; Sha, F.; and DeDeo, S. In ICML, 2013.
link   bibtex  
Activation Cascades in Structured Populations. Galstyan, A. In Handbook of Human Computation, pages 779–789. Springer, New York, NY, 2013.
link   bibtex  
Latent self-exciting point process model for spatial-temporal networks. Cho, Y.; Galstyan, A.; Brantingham, P J.; and Tita, G. arXiv preprint arXiv:1302.2671. 2013.
link   bibtex  
  2012 (5)
Adaptive agents on evolving networks. Kianercy, A.; Galstyan, A.; and Allahverdyan, A. E In AAMAS, pages 1391–1392, 2012.
link   bibtex   4 downloads  
Dynamics of Boltzmann Q learning in two-player two-action games. Kianercy, A.; and Galstyan, A. Physical Review E, 85(4): 041145. 2012.
link   bibtex   5 downloads  
Generative models for spatial-temporal processes with applications to predictive criminology. Cho, Y; Galstyan, A.; Brantingham, J.; and Tita, G. . 2012.
link   bibtex  
Information-Theoretic Measures of Influence Based on Content Dynamics. Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1208.4475. 2012.
link   bibtex   7 downloads  
Statistical tests for contagion in observational social network studies. Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1211.4889. 2012.
link   bibtex  
  2011 (9)
Information Transfer in Social Media. Ver Steeg, G.; and Galstyan, A. . 2011.
link   bibtex   1 download  
Co-evolution of selection and influence in social networks. Cho, Y.; Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1106.2788. 2011.
link   bibtex   15 downloads  
Social mechanics: An empirically grounded science of social media. Lerman, K.; Galstyan, A.; Ver Steeg, G.; and Hogg, T. In Proceedings of the International AAAI Conference on Web and Social Media, volume 5, pages 13–22, 2011.
link   bibtex  
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs. Allahverdyan, A.; and Galstyan, A. In Neural Information Processing Systems (NIPS)., 2011.
link   bibtex  
A Sequence of Relaxations Constraining Hidden Variable Models. Ver Steeg, G.; and Galstyan, A. . 2011.
link   bibtex   5 downloads  
Statistical mechanics of semi-supervised clustering in sparse graphs. Ver Steeg, G.; Galstyan, A.; and Allahverdyan, A. E Journal of Statistical Mechanics: Theory and Experiment, 2011(08): P08009. 2011.
link   bibtex   3 downloads  
Le Chatelier's principle in replicator dynamics. Allahverdyan, A. E; and Galstyan, A. Physical Review E, 84(4): 041117. 2011.
link   bibtex  
Spatio-Temporal Nonlinear Filtering With Applications to Information Assurance and Counter Terrorism. Rozovsky, B.; Tartakovsky, A.; Bertozzi, A; Galstyan, A; Medioni, G; Papadopoulos, C; and Veeravalli, V Technical Report BROWN UNIV PROVIDENCE RI DIV OF APPLIED MATHEMATICS, 2011.
link   bibtex  
Stochastic models of social media dynamics. Lerman, K.; Galstyan, A.; Ver Steeg, G.; and Hogg, T. In 5th International AAAI Conference on Weblogs and Social Media (ICWSM), Barcelona, Spain, 2011.
link   bibtex  
  2010 (4)
Towards modeling social and content dynamics in discussion forums. Kim, J.; and Galstyan, A. In Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media, pages 13–14, 2010.
link   bibtex  
Replicator Dynamics of Coevolving Networks. Galstyan, A.; Kianercy, A.; and Allahverdyan, A. In 2010 AAAI Fall Symposium Series, 2010.
link   bibtex   1 download  
Ruling out latent homophily in social networks. Ver Steeg, G.; and Galstyan, A. NIPSWorkshop on Social Computing. 2010.
link   bibtex  
Community detection with and without prior information. Allahverdyan, A. E; Ver Steeg, G.; and Galstyan, A. EPL (Europhysics Letters), 90(1): 18002. 2010.
link   bibtex   3 downloads  
  2009 (3)
Maximizing influence propagation in networks with community structure. Galstyan, A.; Musoyan, V.; and Cohen, P. Physical Review E, 79(5): 056102. 2009.
link   bibtex   1 download  
Tentacles: Self-configuring robotic radio networks in unknown environments. Chiu, H. C. H.; Ryu, B.; Zhu, H.; Szekely, P.; Maheswaran, R.; Rogers, C.; Galstyan, A.; Salemi, B.; Rubenstein, M.; and Shen, W. In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1383–1388, 2009. IEEE
link   bibtex  
On maximum a posteriori estimation of hidden Markov processes. Allahverdyan, A.; and Galstyan, A. arXiv preprint arXiv:0906.1980. 2009.
link   bibtex   5 downloads  
  2008 (4)
Influence Propagation in Modular Networks. Galstyan, A.; and Cohen, P. R In AAAI Spring Symposium: Social Information Processing, pages 21–23, 2008.
link   bibtex  
Comparing Diffusion Models for Graph–Based Semi–Supervised Learning. Galstyan, A.; and Cohen, P. R In 6th International Workshop on Mining and Learning with Graphs, 2008.
link   bibtex  
Analysis of social voting patterns on digg. Lerman, K.; and Galstyan, A. In Proceedings of the first workshop on Online social networks, pages 7–12, 2008.
link   bibtex   4 downloads  
Top-down vs bottom-up methodologies in multi-agent system design. Crespi, V.; Galstyan, A.; and Lerman, K. Autonomous Robots, 24(3): 303–313. 2008.
link   bibtex  
  2007 (2)
Cascading dynamics in modular networks. Galstyan, A.; and Cohen, P. Physical Review E, 75(3): 036109. 2007.
link   bibtex   5 downloads  
Empirical comparison of “hard” and “soft” label propagation for relational classification. Galstyan, A.; and Cohen, P. R In International Conference on Inductive Logic Programming, pages 98–111, 2007. Springer, Berlin, Heidelberg
link   bibtex