I am a PhD student in MLD within school of computer science at CMU working under Prof. Eric Xing.

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

Publications (dblp, scholar)

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)
* denotes equal contribution
    • 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
    • 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)
    • 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)  
    • 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
    • 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)
    • 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)
    • 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), 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
    • 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
    • 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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