Piyush Rai

Assistant Professor
Dr. Deep Singh and Daljeet Kaur Faculty Fellow
Computer Science & Engineering, IIT Kanpur

Adjunct Assistant Professor
Electrical and Computer Engineering, Duke University

Email: firstname AT cse DOT iitk DOT ac DOT in
Office: KD-319, CSE Department, IIT Kanpur

I'm an Assistant Professor in Computer Science & Engineering at Indian Institute of Technology, Kanpur. I'm also an Adjunct Faculty in the Electrical & Computer Engineering department at Duke University. I did my Ph.D. (2007-2012) in Computer Science from School of Computing, University of Utah, and B.Tech. in Computer Science and Engineering from IIT-BHU, Varanasi.

I work in the area of machine learning and Bayesian statistics. My research is primarily on probabilistic modeling of massive and complex data. My research focuses on inferring compact latent structures and feature representations from such data and leveraging these to better understand/summarize the data and make better predictions/decisions.

Recent Teaching

Recent Publications (full list)

  1. Deep Generative Models for Relational Data with Side Information
    With: Changwei Hu and Lawrence Carin
    ICML 2017, Sydney, Australia

  2. Scalable Generative Models for Multi-label Learning with Missing Labels
    With: Vikas Jain and Nirbhay Modhe
    ICML 2017, Sydney, Australia

  3. A Simple Exponential Family Framework for Zero-Shot Learning
    With: Vinay Verma
    ECML 2017, Skopje, Macedonia

  4. A Probabilistic Framework for Zero-Shot Multi-Label Learning
    With: Abhilash Gaure, Aishwarya Gupta, and Vinay Verma
    UAI 2017, Sydney, Australia

  5. Non-negative Inductive Matrix Completion for Discrete Dyadic Data [pdf]
    AAAI 2017, San Francisco, USA

  6. Deep Metric Learning with Data Summarization [pdf]
    With: Wenlin Wang, Changyou Chen, Wenlin Chen, and Lawrence Carin
    ECML 2016, Riva del Garda, Italy

  7. Topic-Based Embeddings for Learning from Large Knowledge Graphs [pdf]
    With: Changwei Hu and Lawrence Carin
    AISTATS 2016, Cadiz, Spain

  8. Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information [pdf]
    With: Changwei Hu and Lawrence Carin
    AISTATS 2016, Cadiz, Spain

  9. Earliness-Aware Deep Convolutional Networks for Early Time Series Classification [pdf]
    With: Wenlin Wang, Changyou Chen, Wenqi Wang, and Lawrence Carin
    arXiv pre-print, 2016

  10. Architecture Adaptive Code-Variant Tuning [pdf]
    With: Saurav Muralidharan, Amit Roy, Mary Hall and Michael Garland
    ASPLOS 2016, Atlanta, Georgia

  11. Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings [pdf]
    With: Changwei Hu, Ricardo Henao, and Lawrence Carin
    NIPS 2015, Montreal, Canada
    Spotlight Presentation

  12. Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors [pdf]
    With: Changwei Hu and Lawrence Carin
    UAI 2015, Amsterdam, The Netherlands
    Plenary Oral Presentation

  13. Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data [pdf]
    With: Changwei Hu, Changyou Chen, Matthew Harding, and Lawrence Carin
    ECML 2015, Porto, Portugal
    Best Student Paper Award

  14. Integrating Features and Similarities: Flexible Models for Heterogeneous Multiview Data [pdf]
    With: Wenzhao Lian, Esther Salazar, and Lawrence Carin
    AAAI 2015, Austin, Texas

  15. Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision [pdf]
    With: Yi Zhen, Hongyuan Zha, and Lawrence Carin
    AAAI 2015, Austin, Texas

  16. Leveraging Features and Networks for Probabilistic Tensor Decomposition [pdf]
    With: Yingjian Wang and Lawrence Carin
    AAAI 2015, Austin, Texas

  17. Scalable Probabilistic Tensor Factorization for Binary and Count Data [pdf]
    With: Changwei Hu, Matthew Harding, and Lawrence Carin
    IJCAI 2015, Buenos Aires, Argentina