My research interests include statistical machine learning, non-parametric Bayesian methods, information retrieval and clustering. I have recently worked on transferring supervision in finite and infinite mixture models (Dirichlet Process) and information retrieval.
I interned at Microsoft Research Redmond in summer of 2014 and got the opportunity to work with great mentors: Rich Caruana, Evelyne Viegas and Mathew Richardson. I also received the prestigious IBM Fellowship for 2013-2015. I have worked as a researcher for 2 years at IBM Research India from 2009 - 2011. I received my Master of Technology degree in Computer Science from IIT Bombay in 2009. I did my Master's thesis in Information Retrieval under the guidance of Prof. Soumen Chakrabarti.
Working Manuscripts
- Contextual Explanation Networks - M. Al-Shedivat, A. Dubey, E.P. Xing (In submission arXiv)
- Recurrent Estimation of Distributions - J. Oliva*, A. Dubey*, B. Poczos, J. Schneider, E. P. Xing (In submission arXiv)
- From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems - M. Sachan, A. Dubey, E.P. Xing (In submission)
* denotes equal contribution
2016- 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)
- Science Question Answering using Instructional Materials - M. Sachan, A. Dubey, E. P. Xing. Association for Computational Linguistics (ACL) 2016. (pdf )
- Estimating Accuracy from Unlabeled Data: A Bayesian Approach - E. Platanios, A. Dubey, T. Mitchell. International Conference of Machine Learning (ICML) 2016. (pdf, supplementary)
- 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)
2015 - 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)
- 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)
- Learning Answer-Entailing Structures for Machine Comprehension - M. Sachan, A. Dubey, M. Richardson, E. P. Xing, Association for Computational Linguistics (ACL) 2015. (pdf) (Best paper nomination, Selected as one of the Outstanding Papers at ACL 2015)
- Parallel Markov chain Monte Carlo for the Indian buffet process. - M. Zhang, A. Dubey and S. Williamson, Advances in Neural Information Processing Systems (NIPS) Workshop on Bayesian Nonparametrics: The Next Generation 2015. (pdf)
2014 - 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 )
- 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)
- 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) (Considered for best paper)
- 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 coming)
2013 - Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models - S. Williamson, A. Dubey and E. P. Xing. The 30th International Conference on Machine Learning (ICML) 2013 [preprint]
- 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. (previous version pdf)
2012 - 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)
2011 - Learning Dirichlet Processes from Partially Observed Groups, A. Dubey, I. Bhattacharya, M. Das, T. Faruquie, and C. Bhattacharyya, IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, 2011. (pdf)
- Diversity in Ranking via Resistive Graph Centers, A. Dubey, S. Chakrabarti and C. Bhattacharyya, 17th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), San Diego, CA, USA, 2011. (pdf)
2010 - 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), Barcelona, Spain, September 2010. (pdf)
2009 - Conditional Models for Non-smooth Ranking Loss Functions, A. Dubey, J. Machchhar, C. Bhattacharyya and S. Chakrabarti, IEEE International Conference on Data Mining (ICDM), Miami, Florida, USA, December 2009. (pdf)
- Efficient and Accurate Local Learning for Ranking, S. Banerjee, A. Dubey, J. Machchhar, S. Chakrabarti, 32nd Annual ACM SIGIR Conference workshop on Learning to Rank for Information Retrieval , Boston, USA, July 2009. (pdf)
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