News

• Our team "coin toss" won the Neurohackathon 2016 (news).

• Our paper was selected as one of 5 outstanding papers at ACL 2015.

• IBM PhD fellowship renewed for 2015-2016. (news).

• Interned at Microsoft Research Redmond in summer of 2014. Got the opportunity to work with great mentors: Rich Caruana, Evelyne Viegas and Mathew Richardson.

• Received IBM PhD fellowship for the academic year 2014-2015.

Old version of the website is here ( http://www.cs.cmu.edu/~akdubey/ ).

Publications (dblp, scholar, semantic)

Area: Deep Networks & Graphical Models [DN&GM], Scalable Inference & Bayesian Nonparametrics [SI&BN], Question Answering & Information Retrieval [QA&IR]

Published

• Distributed, partially collapsed MCMC for Bayesian Nonparametrics - A. Dubey*, M. Zhang*, E. P. Xing, S. A. Williamson. International Conference on Artificial Intelligence and Statistics (AISTATS) 2020. [pdf, DN&GM]

• Contextual Explanation Networks - M. Al-Shedivat, A. Dubey, E.P. Xing. Journal of Machine Learning Research, (JMLR) 2020. [arXiv:1705.10301, DN&GM]

• Discourse in Multimedia: A Case Study in Information Extraction - M. Sachan, A. Dubey, E. Hovy, T. Mitchell, D. Roth and E. P. Xing. (Computational Linguistics Journal 2020, pdf, QA&IR)

• Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems - M. Sachan, A. Dubey, T. Mitchell, D. Roth, E. Xing. Advances in Neural Information Processing Systems (NeurIPS) 2018. [pdf, QA&IR]

• Transformation Autoregressive Networks - J. Oliva, A. Dubey, M. Zaheer, B. Poczos, R. Salakhutdinov, E. P. Xing, J. Schneider. International Conference on Machine Learning (ICML) 2018 [arXiv:1801.09819, DN&GM]

• From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems -- M. Sachan, A. Dubey, E.P. Xing. Conference on Empirical Methods in Natural Language Processing (EMNLP) 2017. [pdf, QA&IR]

• Variance Reduction in Stochastic Gradient Langevin Dynamics -- A. Dubey*, S. Reddi*, S. Williamson, B. Poczos, A. Smola, E. Xing. Advances in Neural Information Processing Systems (NIPS) 2016. [pdf, SI&BN]

• Bayesian Nonparametric Kernel-Learning -- A. Dubey*, J. Oliva*, A. Wilson, B. Poczos, J. Schneider, E. P. Xing. International Conference on Artificial Intelligence and Statistics (AISTATS) 2016. [pdf, SI&BN]

• Estimating Accuracy from Unlabeled Data: A Bayesian Approach -- E. Platanios, A. Dubey, T. Mitchell. International Conference of Machine Learning (ICML) 2016. [pdf, supplementary, SI&BN]

• Science Question Answering using Instructional Materials -- M. Sachan, A. Dubey, E. P. Xing. Association for Computational Linguistics (ACL) 2016. [pdf, QA&IR]

• Learning Answer-Entailing Structures for Machine Comprehension -- M. Sachan, A. Dubey, M. Richardson, E. P. Xing, Association for Computational Linguistics (ACL) 2015. [pdf, QA&IR]

• Large-scale Distributed Dependent Nonparametric Trees -- Z. Hu, Q. Ho, A. Dubey, E. P. Xing, The 32th International Conference on Machine Learning (ICML) 2015. [pdf, SI&BN]

• Large-scale randomized-coordinate descent methods with non-separable linear constraints -- S. Reddi, A. Hefny, A. Dubey, C. Downey, S. Sra. International Conference on Conference on Uncertainty in Artificial Intelligence (UAI) 2015. [arXiv:1409.2617, SI&BN]

• Dependent nonparametric trees for dynamic hierarchical clustering -- A. Dubey*, Q. Ho*, S.Williamson and E. P. Xing, Advances in Neural Information Processing Systems (NIPS) 2014 [pdf, DAP, SI&BN]

• Parallel Markov Chain Monte Carlo for Pitman-Yor Mixture Models -- A. Dubey, S.Williamson and E. P. Xing, International Conference on Conference on Uncertainty in Artificial Intelligence (UAI) 2014. [pdf, SI&BN]

• Spatial Compactness meets Topical Consistency: Jointly modeling Links and Content for Community Detection -- M. Sachan, A. Dubey, S. Srivastava, E. P. Xing and E. Hovy, International Conference on Web Search and Data Mining (WSDM) 2014. [pdf, SI&BN]

• Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models -- S. Williamson, A. Dubey, E. P. Xing. The 30th International Conference on Machine Learning (ICML) 2013 [pdf, SI&BN]

• A Non-parametric Mixture Model for Topic Modeling Over Time -- A. Dubey, A. Hefny, S. Williamson, E. P. Xing, Proceedings of The Thirteenth SIAM International Conference on Data Mining (SDM) 2013. [pdf, SI&BN]

• AUSUM: approach for unsupervised bug report summarization -- S. Mani, R. Catherine, V. S. Sinha, A. Dubey, ACM 20th International Symposium on the Foundations of Software Engineering (SIGSOFT) 2012. [pdf, QA&IR]

• Learning Dirichlet Process from Partially Observed Groups -- A. Dubey, I. Bhattacharya, M. Das, T. Faruqie, and C. Bhattacharyya, IEEE International Conference on Data Mining (ICDM) 2011. [pdf, SI&BN]

• Diversity in Ranking via Resistive Graph Centers -- A. Dubey, S. Chakrabarti, C. Bhattacharyya, 17th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD) 2011. [pdf, QA&IR]

• A Cluster-Level Semi-Supervision Model for Interactive Clustering -- A. Dubey, I. Bhattacharya, S. Godbole, The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2010. [pdf, QA&IR]

• Conditional Models for Non-smooth Ranking Loss Functions -- A. Dubey, J. Machchhar, C. Bhattacharyya and S. Chakrabarti, IEEE International Conference on Data Mining (ICDM) 2009. [pdf, QA&IR]

* denotes equal contribution

Workshop & Other Publications

• Contextual Explanation Networks Enable Integrated Analysis Of Imaging And Genomic Data - B. Lengerich, M. Al-Shedivat, A. Dubey, A. Alavi, J. Williams and E. Xing. ISMB 2018 Abstracts

• Recurrent Estimation of Distributions - A. Dubey*, J. Oliva*, B. Poczos, J. Schneider, E. P. Xing - [arXiv:1705.10750, DN&GM]

• The Intriguing Properties of Model Explanations - M. Al-Shedivat, A. Dubey, E.P. Xing. Advances in Neural Information Processing Systems (NIPS) 2017 Symposium on Interpretable Machine Learning [pdf]

• Patient Specific Survival Prediction with Explanations - M. Al-Shedivat, A. Dubey, E.P. Xing. Advances in Neural Information Processing Systems (NIPS) 2017 ML for Health Workshop (ML4H) [pdf]

• Parallel Markov chain Monte Carlo for the Indian buffet process - M. Zhang, A. Dubey, S. Williamson, Advances in Neural Information Processing Systems (NIPS) Workshop on Bayesian Nonparametrics: The Next Generation 2015. [pdf]

• Integrating Transition-based and Graph-based Parsing Using Integer Linear Programming - A. Hefny, A. Dubey, S. J. Reddy, Advances in Neural Information Processing Systems 28 (NIPS ) Workshop Modern ML + NLP [pdf]

• Efficient and Accurate Local Learning for Ranking - S. Banerjee, A. Dubey, J. Machchhar, S. Chakrabarti, 32^nd Annual ACM SIGIR Conference workshop on Learning to Rank for Information Retrieval , Boston, USA, July 2009. [pdf]