Rishabh Iyer's Website

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Welcome to Rishabh Iyer's webpage

Rishabh Iyer is a Ph.D candidate at the University of Washington, Seattle, where he works with Prof. Jeff Bilmes. He did an internship in the summer of 2014 at the Machine Learning Group at Microsoft Research, Redmond, where he worked with Max Chickering and Chris Meek. In the summer of 2012, he did an internship at MSR, Redmond where he worked with Dr. Matthai Phillipose at the Mobility and Networking Research Group. Prior to coming to UW, he finished is B.Tech in Electrical Engineering at the Indian institute of Technology, Bombay. At IIT Bombay, he worked with Prof. Subhasis Chaudhuri. He also did an internship in 2010 at the Computer Science Department at Simon Fraser University, where he worked with Prof. Torsten Möller.

His research interests are at the intersection of Discrete optimization and Machine Learning, and particularly in subset selection problems in machine learning like summarization, data subset selection, segmentation, feature selection, sensor placement, image correspondence etc. His work is focussed on a particular class of discrete optimization problems called Submodular Optimization, which on one hand are powerful, flexible and representable models in many real world applications, but on the other hand also admit algorithms with often strong theoretical guarantees. His work focusses on investigating certain theoretical characterizations of submodular functions and exploiting these in machine learning applications. One significant thread of his research revolves around providing faster, scalable and more practical algorithms for a large class of submodular optimization problems, and applying them to several real world problems. He is keenly interested in connecting theory with practice, and in various applications of submodular optimization in computer vision, natural language processing, speech and information retrieval.

He recently received the Microsoft Research Fellowship award, in 2014. He also received the Facebook Fellowship award (declined in the favor of the Microsoft one), the Yang Outstanding Doctoral student award, and best paper awards at the top tier Machine Learning conferences, Neural Information Processing Systems (NIPS) and International Conference for Machine Learning (ICML). Here is a formal bio in third person.

For more information, please see his recent publications (DBLP, Google Scholar Profile), or you can contact him at rkiyer at u.washington.edu. Here is a link to his Curriculum Vitae.


Journal Publications

(J1) Rishabh Iyer, Rushikesh Borse and Subhasis Chaudhuri, Embedding capacity estimation of reversible watermarking schemes, In Sadhana 39 (Part 6), 2014 PDF.

Conference Publications

(C12) Rishabh Iyer and Jeff A. Bilmes, Submodular Point Processes with Applications in Machine Learning To appear In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), San-Diego, United States, May - 2015

(C11) Yoshinobu Kawahara, Rishabh Iyer and Jeff Bilmes On Approximate Non-submodular Minimization via Tree-Structured Supermodularity, To appear In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), San-Diego, United States, May - 2015.

(C10) Sebastian Tschiatschek, Rishabh Iyer, Hoachen Wei and Jeff Bilmes, Learning Mixtures of Submodular Functions for Image Collection Summarization, In Advances of Neural Information Processing Systems (NIPS), Montreal, Canada, December - 2014 PDF.

(C9) Rishabh Iyer, Stefanie Jegelka and Jeff Bilmes, Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization, In Uncertainity in Artificial Intelligence (UAI), Quebec City, Canada, July - 2014 PDF, Extended Version.

(C8) Kai Wei, Rishabh Iyer, and Jeff Bilmes, Fast Multi-stage Submodular Maximization, In Proc. International Conference on Machine Learning (ICML), Beijing, China, June - 2014 PDF, Extended Version.

(C7) Rishabh Iyer and Jeff A. Bilmes, Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints, In Advances of Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December - 2013 PDF, Arxiv Extended Version, Slides, Video (Winner of the Outstanding paper award)

(C6) Rishabh Iyer, Stefanie Jegelka, and Jeff A. Bilmes, Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions, In Advances of Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December - 2013 PDF, Arxiv Extended Version, Poster

(C5) Rishabh Iyer and Jeff A. Bilmes, The Lovasz-Bregman Divergence and connections to rank aggregation, clustering and web ranking , In Uncertainity in Artificial Intelligence (UAI), Bellevue, Washington, July - 2013 PDF, Extended Version, Slides (selected for oral presentation, 11% Acceptance rate).

(C4) Rishabh Iyer, Stefanie Jegelka, and Jeff A. Bilmes, Fast Semidifferential-based Submodular Function Optimization, In Proc. International Conference on Machine Learning (ICML), Atlanta, Georgia, June - 2013 PDF, Extended Version, Arxiv, Slides, Code, Video (Winner of the Best paper award).

(C3) Rishabh Iyer and Jeff A. Bilmes, Submodular-Bregman and the Lovasz-Bregman Divergences with Applications, In Advances of Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December - 2012 PDF, Extended Version, Poster.

(C2) Rishabh Iyer and Jeff A. Bilmes, Algorithms for Approximate Minimization of the Difference between Submodular Functions, In Uncertainity in Artificial Intelligence (UAI), Catalina Islands, CA, August - 2012 PDF, Extended Version, Slides, Code (selected for oral presentation, 9% Acceptance rate).

(C1) Shah Ronak, Rishabh Iyer and Subhasis Chaudhuri, Object Mining for Large Video Data, In Proc. British Machine Vision Conference (BMVC), Dundee, Scotland, United Kingdom, August - 2011, PDF (selected for oral presentation - 8% Acceptance rate).

Workshops and Preprints

(W5) Rishabh Iyer and Jeff A. Bilmes, Near Optimal algorithms for constrained submodular programs with discounted cooperative costs, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML), Lake Tahoe, Nevada, December - 2014 PDF.

(W4) Rishabh Iyer and Jeff A. Bilmes, Submodular Point Processes, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML), Lake Tahoe, Nevada, December - 2014 PDF.

(W3) Yoshinobu Kawahara, Rishabh Iyer and Jeff Bilmes On Approximate Non-submodular Minimization via Tree-Structured Supermodularity, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML), Lake Tahoe, Nevada, December - 2014 PDF.

(W2) Rishabh Iyer, Stefanie Jegelka and Jeff A. Bilmes, Mirror Descent-Like Algorithms for Submodular Optimization, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML): Structure and Scalability, Lake Tahoe, Nevada, December - 2012 PDF, Poster.

(P1) Rishabh Iyer, Rushikesh Borse, Shah Ronak and Subhasis Chaudhuri, Embedding Capacity estimation for pixel pair based watermarking schemes, Arxiv Preprint.

(W1) Rishabh Iyer and Torsten Möller, A spatial domain optimization for sampling pointsets, MITACS Globalink Research Symposium, University of British Columbia, Canada - 2010. PDF

Professional Activities

Reviewer: International Conference of Machine Learning (ICML) 2013/2014, Neural Information Processing Systems (NIPS) 2013/2014, Journal of Machine Learning Research (JMLR).

Collaborators (Past and Present)

Jeff Bilmes, Stefanie Jegelka, Yoshinobu Kawahara, Kai Wei, Sebastian Tschiatschek, Chris Meek, Max Chickering, Patrice Simard, Ganesh Ramakrishnan, Matthai Phillipose, Bethany Herwaldt, Subhasis Chaudhuri, Torsten Möller.

Talks

(T6) Submodular Combinatorial Problems in Machine Learning: Algorithms and Applications, Invited Talk at IIT Bombay, IISc Bangalore, MSR Bangalore, IIT Gandhinagar, TOPS Seminar (UW), Yahoo! Machine Learning Lunch (UW) (See related video)

(T5) Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints, NIPS-2013, Lake Tahoe, Dec 2013. (see video of this talk)

(T4) The Lovasz-Bregman Divergence and connections to rank aggregation, clustering and web ranking, UAI-2013, Bellevue, July 2013.

(T3) Fast Semidifferential-based Submodular Function Optimization, ICML-2013, Atlanta, June 2013. (see video of this talk)

(T2) Submodular Bregman divergences and Lovasz-Bregman divergences with Applications to Machine Learning, Seminar, IIT Bombay, March 2013.

(T1) Algorithms for Approximate Minimization of the Difference between Submodular Functions, UAI-2012, Catalina Island, August 2012.

Fun Stuff

My hobies include biking, hiking, squash, racquetball, tennis, cooking, meditation, and philosophical discussions. On weekends, I love to volunteer at the Vedic Cultural Center, Sammamish. I am strong advocate of work-life balance!
You can find me on facebook, LinkedIn and Google.
Finally this is the person I owe my life to!