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  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.
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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.
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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.
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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.
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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
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DEAM: Dialogue Coherence Evaluation using AMR-based Semantic Manipulations. Ghazarian, S.; Wen, N.; Galstyan, A.; and Peng, N. In ACL 2022, 2022.
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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.
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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.
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  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.
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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.
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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.
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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.
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Discovering Higher-Order Interactions Through Neural Information Decomposition. Reing, K.; Ver Steeg, G.; and Galstyan, A. Entropy, 23(1): 79. 2021.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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
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Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2107.01598. 2021.
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Attributing Fair Decisions with Attention Interventions. Mehrabi, N.; Gupta, U.; Morstatter, F.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2109.03952. 2021.
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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.
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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.
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Cognitively Inspired Learning of Incremental Drifting Concepts. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2110.04662. 2021.
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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.
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Failure Modes of Domain Generalization Algorithms. Galstyan, T.; Harutyunyan, H.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2111.13733. 2021.
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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.
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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
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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.
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Exacerbating Algorithmic Bias through Fairness Attacks. Mehrabi, N.; Naveed, M.; Morstatter, F.; and Galstyan, A. In AAAI'21, 2021.
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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.
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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.
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  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.
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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.
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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.
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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.
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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
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Anchor Attention for Hybrid Crowd Forecasts Aggregation. Huang, Y.; Abeliuk, A.; Morstatter, F.; Atanasov, P.; and Galstyan, A. arXiv preprint arXiv:2003.12447. 2020.
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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.
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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.
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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.
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Sequential unsupervised domain adaptation through prototypical distributions. Rostami, M.; and Galstyan, A. . 2020.
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Robust Classification under Class-Dependent Domain Shift. Galstyan, T.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2007.05335. 2020.
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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.
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Quantifying machine influence over human forecasters. Abeliuk, A.; Benjamin, D. M; Morstatter, F.; and Galstyan, A. Scientific reports, 10(1): 1–14. 2020.
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Likelihood ratio exponential families. Brekelmans, R.; Nielsen, F.; Makhzani, A.; Galstyan, A.; and Steeg, G. V. arXiv preprint arXiv:2012.15480. 2020.
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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.
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Latent Embeddings of Point Process Excitations. Marmarelis, M. G; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:2005.02515. 2020.
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Domain Agnostic Prototypical Distribution for Unsupervised Model Adaptation. Rostami, M.; and Galstyan, A. . 2020.
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Learning a Max-Margin Classifier for Cross-Domain Sentiment Analysis. Rostami, M.; and Galstyan, A. . 2020.
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Sequential Model Adaptation Using Domain Agnostic Internal Distributions. Rostami, M.; and Galstyan, A. arXiv preprint arXiv:2007.00197. 2020.
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  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.
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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.
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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
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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
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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
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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.
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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.
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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
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Efficient covariance estimation from temporal data. Harutyunyan, H.; Moyer, D.; Khachatrian, H.; Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1905.13276. 2019.
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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.
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Fast structure learning with modular regularization. Ver Steeg, G.; Harutyunyan, H.; Moyer, D.; and Galstyan, A. Advances in Neural Information Processing Systems, 32. 2019.
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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.
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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.
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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.
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  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.
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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.
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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.
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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.
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A Forest Mixture Bound for Block-Free Parallel Inference. Lawton, N.; Galstyan, A.; and Steeg, G. V. In Uncertainty in Artificial Intelligence (UAI), 2018.
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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.
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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.
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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.
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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.
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  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.
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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
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Highly Accurate Link Prediction in Networks Using Stacked Generalization. Ghasemian, A.; Galstyan, A.; and Clauset, A. . 2017.
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Sifting Common Information from Many Variables. Ver Steeg, G.; Gao, S.; Reing, K.; and Galstyan, A. In IJCAI, pages 2885–2892, 2017.
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  2016 (16)
The information sieve. Ver Steeg, G.; and Galstyan, A. In International Conference on Machine Learning, pages 164–172, 2016. PMLR
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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.
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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.
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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.; and Menczer, F. Computer, 49(6): 38–46. 2016.
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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
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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
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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.
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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.
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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.
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Emergence of leadership in communication. Allahverdyan, A. E; and Galstyan, A. PloS one, 11(8): e0159301. 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 International semantic web conference, pages 649–667, 2016. Springer, Cham
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Sifting common information from many variables. Steeg, G. V.; Gao, S.; Reing, K.; and Galstyan, A. arXiv preprint arXiv:1606.02307. 2016.
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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.
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Variational information maximization for feature selection. Gao, S.; Ver Steeg, G.; and Galstyan, A. Advances in neural information processing systems, 29. 2016.
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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.
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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
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  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.
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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.
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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
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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
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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.
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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
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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.
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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
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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
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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
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  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.
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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.
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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.
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Where and why users “check in”. Cho, Y.; Ver Steeg, G.; and Galstyan, A. In Proc. of AAAI, volume 14, pages 269–275, 2014.
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Discovering structure in high-dimensional data through correlation explanation. Ver Steeg, G.; and Galstyan, A. Advances in Neural Information Processing Systems, 27. 2014.
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Opinion dynamics with confirmation bias. Allahverdyan, A. E; and Galstyan, A. PloS one, 9(7): e99557. 2014.
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Mixed Membership Blockmodels for Dynamic Networks with Feedback. Cho, Y.; Ver Steeg, G.; and Galstyan, A. 2014.
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Maximally Informative Hierarchical Representations of High-Dimensional Data. Ver Steeg, G.; and Galstyan, A. In Artificial Intelligence and Statistics (AISTATS), 2014.
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Understanding confounding effects in linguistic coordination: an information-theoretic approach. Gao, S.; Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1412.0696. 2014.
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  2013 (10)
Continuous strategy replicator dynamics for multi-agent q-learning. Galstyan, A. Autonomous agents and multi-agent systems, 26(1): 37–53. 2013.
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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.
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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.
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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.
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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.
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Coevolutionary networks of reinforcement-learning agents. Kianercy, A.; and Galstyan, A. Physical Review E, 88(1): 012815. 2013.
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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
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Demystifying information-theoretic clustering. Ver Steeg, G.; Galstyan, A.; Sha, F.; and DeDeo, S. In ICML, 2013.
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Activation Cascades in Structured Populations. Galstyan, A. In Handbook of Human Computation, pages 779–789. Springer, New York, NY, 2013.
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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.
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  2012 (5)
Adaptive agents on evolving networks. Kianercy, A.; Galstyan, A.; and Allahverdyan, A. E In AAMAS, pages 1391–1392, 2012.
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Dynamics of Boltzmann Q learning in two-player two-action games. Kianercy, A.; and Galstyan, A. Physical Review E, 85(4): 041145. 2012.
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Generative models for spatial-temporal processes with applications to predictive criminology. Cho, Y; Galstyan, A.; Brantingham, J.; and Tita, G. . 2012.
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Information-Theoretic Measures of Influence Based on Content Dynamics. Steeg, G. V.; and Galstyan, A. arXiv preprint arXiv:1208.4475. 2012.
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Statistical tests for contagion in observational social network studies. Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1211.4889. 2012.
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  2011 (9)
Information Transfer in Social Media. Ver Steeg, G.; and Galstyan, A. . 2011.
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Co-evolution of selection and influence in social networks. Cho, Y.; Ver Steeg, G.; and Galstyan, A. arXiv preprint arXiv:1106.2788. 2011.
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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.
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Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs. Allahverdyan, A.; and Galstyan, A. In Neural Information Processing Systems (NIPS)., 2011.
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A Sequence of Relaxations Constraining Hidden Variable Models. Ver Steeg, G.; and Galstyan, A. . 2011.
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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.
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Le Chatelier's principle in replicator dynamics. Allahverdyan, A. E; and Galstyan, A. Physical Review E, 84(4): 041117. 2011.
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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.
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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.
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  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.
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Replicator Dynamics of Coevolving Networks. Galstyan, A.; Kianercy, A.; and Allahverdyan, A. In 2010 AAAI Fall Symposium Series, 2010.
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Ruling out latent homophily in social networks. Ver Steeg, G.; and Galstyan, A. NIPSWorkshop on Social Computing. 2010.
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Community detection with and without prior information. Allahverdyan, A. E; Ver Steeg, G.; and Galstyan, A. EPL (Europhysics Letters), 90(1): 18002. 2010.
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  2009 (3)
Maximizing influence propagation in networks with community structure. Galstyan, A.; Musoyan, V.; and Cohen, P. Physical Review E, 79(5): 056102. 2009.
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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
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On maximum a posteriori estimation of hidden Markov processes. Allahverdyan, A.; and Galstyan, A. arXiv preprint arXiv:0906.1980. 2009.
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  2008 (4)
Influence Propagation in Modular Networks. Galstyan, A.; and Cohen, P. R In AAAI Spring Symposium: Social Information Processing, pages 21–23, 2008.
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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.
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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.
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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.
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  2007 (2)
Cascading dynamics in modular networks. Galstyan, A.; and Cohen, P. Physical Review E, 75(3): 036109. 2007.
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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
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