Talks

The list below gives links to slides for some talks I have delivered in the past. The format for these slides is not uniform – slides for certain talks were prepared in PDF directly using beamer, for others, Microsoft PowerPoint was used but slides are presented in PDF format, for still others, the original PPTX slides are presented.

Invited Talks

  1. DECAF: Deep Extreme Classification with Label Features,
    Amazon Research Days, Virtual Event, December 16 – 17, 2020.
    [pptx-slides]

  2. Beyond Convenience: Beyond Convexity,
    Mini Symposium on Computation and Optimization in the Sciences and Engineering, IIT Kanpur, February 3-4, 2016.
    [pdf-slides]

  3. An Introduction to Concentration Inequalities and Statistical Learning Theory,
    MSR India Summer School 2015 on Machine Learning, June 15-26, 2015.
    [pptx-slides]

  4. Algorithms for Processing Massive Data Sets,
    3rd International Symposium, Graduate School of Information, Production and Systems, Waseda University, Japan, March 10-11, 2010.
    [pdf-slides]

  5. Support Vector Machines and their Applications,
    Summer School on Expert Systems and Their Applications, Indian Institute of Information Technology, Allahabad, June 10-14, 2009.
    [pdf-slides]

Lectures, Seminars

  1. Some Recent Advances in Non-convex Optimization,
    Amazon Machine Learning Group, Bangalore, June 20, 2016.
    The Data Science Institute, Columbia University, July 20, 2016.
    [pdf-slides]

  2. Extreme Classification - a Tale of Two Approaches,
    Amazon Machine Learning Group, Bangalore, June 20, 2016.

  3. Learning with Pairwise Losses,
    Course Lecture for E0 370: Statistical Learning Theory, October 29, 2013.
    (Instructor: Prof. Shivani Agarwal, Dept. of CSA, IISc)
    [pdf-slides]

  4. Online Learning with Pairwise Loss Functions,
    MLSIG Seminar Series, Dept. of CSA, IISc, September 12, 2013.
    [pdf-slides]

  5. A pre-Weekend Talk on Online Learning,
    TGIF Talk Series, Microsoft Research India, August 23, 2013.
    [pdf-slides]

  6. Explicit Feature Methods for Accelerated Kernel Learning,
    Machine Learning and Optimization Group, Microsoft Research India, August 14, 2013.
    [pdf-slides]

  7. Accelerated Kernel Learning,
    Department of CSE, IIT Gandhinagar, November 27, 2012.
    [pdf-slides]

  8. Topics in Kernel Learning,
    Alan Turing Centenary Year Celebrations, Department of CSE, IIT Kanpur, October 10, 2012.
    [pdf-slides]

  9. Random Features for Kernel Learning,
    Mysore Park Wokshop on Machine Learning, Infosys Campus Mysore, August 3, 2012.
    [pdf-slides]

  10. Learning with Similarity Functions,
    Machine Learning and Optimization Group, Microsoft Research India, July 18, 2012.
    [pdf-slides]

  11. Similarity-based Learning via Data Driven Embeddings,
    CSE Departmental Colloquia, CSE/IITK, October 28, 2011.
    [pdf-slides]

  12. Learning in Indefiniteness,
    A presentation I gave for my PhD State of the Art seminar, CSE/IITK, August 2, 2010.
    [pdf-slides]

  13. An Introduction to Computational Learning Theory,
    SIGML Seminar Series, CSE/IITK, January 23, 2010.
    [pdf-slides]

  14. How to Hoodwink a Halfspace,
    A presentation I gave for my PhD comprehensive examination, CSE/IITK, January 12, 2010.
    [pdf-slides]

  15. Metric Embeddings and Applications in Computer Science,
    SIGTACS Seminar Series, CSE/IITK, January 10, 2009.
    [pdf-slides]