Publications

THESIS

Rishabh Iyer, Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications, Ph.D Dissertation, University of Washington, Seattle, June 2015

Pre-Prints

  1. Suraj Kothawade (UT Dallas)*; Nathan Beck (UT Dallas); Krishnateja Killamsetty (University of Texas at Dallas); Rishabh Iyer (University of Texas at Dallas), SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios, arXiv: 2107.00717

  2. Krishnateja Killamsetty, Xujiang Zhou, Feng Chen, and Rishabh Iyer, RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning, arXiv: 2106.07760

  3. Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, and Rishabh Iyer, Effective Evaluation of Deep Active Learning on Image Classification Tasks, arXiv: 2106.15324

  4. Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer, PRISM: A Unified Framework of Parameterized Submodular Information Measures for Targeted Data Subset Selection and Summarization, arXiv:2103.00128

  5. Xujiang Zhao, Killamsetty Krishnateja, Rishabh Iyer, Feng Chen, Robust Semi-Supervised Learning with Out of Distribution Data, arXiv:2010.03658

  6. Krishnateja Killamsetty, Changbin Li, Chen Zhou, Rishabh Iyer, and Feng Chen, A Reweighted Meta Learning Framework for Robust Few Shot Learning, arXiv:2011.06782

  7. Suraj Kothawade, Jiten Girdhar, Chandrashekar Lavania, and Rishabh Iyer, Deep Submodular Networks for Extractive Data Summarization, arXiv:2010.08593

Peer-Reviewed Publications (Conferences & Journals)

  1. Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer, GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Deep Model Training, To Appear in ICML 2021 (21% Acceptance Rate)

  2. Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan, and Abir De, Training Data Subset Selection for Regression with Controlled Validation Error, To Appear in ICML 2021 (21% Acceptance Rate) [Project Page]

  3. Krishnateja Killamsetty, S Durga, Ganesh Ramakrishnan, and Rishabh Iyer, GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning, 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (21% Acceptance Rate)

  4. Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, and Rishabh Iyer, Data Programming using Semi-Supervision and Subset Selection, To Appear in Findings of ACL, 2021 (Long Paper)

  5. Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, and Rishabh Iyer, Rule Augmented Unsupervised Constituency Parsing, To Appear in Findings of ACL, 2021 (Short Paper)

  6. Chandrashekhar Lavania, Kai Wei, Rishabh Iyer, and Jeff Bilmes, A Practical Online Framework with a Fixed Memory Budget for Extracting Running Video Summaries, SIAM International Conference on Data Mining, SDM 2021 (21.25% Acceptance Rate) Video Demo of the System in Action

  7. Himanshu Asnani, Jeff Bilmes, and Rishabh Iyer, Independence Properties of Generalized Submodular Information Measures, 2021 IEEE International Symposium on Information Theory, ISIT 2021 (Virtual Conference)

  8. Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes, and Himanshu Asnani, Submodular Combinatorial Information Measures with Applications in Machine Learning, The 32nd International Conference on Algorithmic Learning Theory, ALT 2021 (29.2% Acceptance Rate) Related Video

  9. Srijita Das, Rishabh Iyer, Sriraam Natarajan, A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation, In CODS-COMAD 2021 (Honorable Mention, Research Track)

  10. Rishabh Iyer and Jeff Bilmes, Concave Aspects of Submodular Functions, In IEEE International Symposium on Information Theory, ISIT 2020 (Longer version of this paper: Polyhedral aspects of submodularity, convexity and concavity, arXiv preprint arXiv:1506.07329)

  11. Rishabh Iyer, Robust Submodular Minimization with Applications to Cooperative Modeling, The 24th European Conference on Artificial Intelligence (ECAI) 2020, Santiago de Compostela, Spain (26.8% Acceptance Rate). Related Video

  12. Rishabh Iyer and Jeff Bilmes, A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems, To Appear in Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan (32.4% Acceptance Rate)

  13. Rishabh Iyer and Jeff Bilmes, Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs, To Appear in Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan (32.4% Acceptance Rate)

  14. Vishal Kaushal, Sandeep Subramanium, Suraj Kothiwade, Rishabh Iyer, and Ganesh Ramakrishnan, A Framework Towards Domain Specific Video Summarization, 7th IEEE Winter Conference on Applications of Computer Vision (WACV) 2019, Hawaii, USA (Long Version, Link to the Video)

  15. Vishal Kaushal, Rishabh Iyer, Suraj Kothiwade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan, Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision, 7th IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 Hawaii, USA (Link to the Video)

  16. Vishal Kaushal, Rishabh Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramkrishnan, Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance, 7th IEEE Winter Conference on Applications of Computer Vision (WACV) 2019, Hawaii, USA (Link to Video)

  17. Rishabh Iyer, Nimit Acharya, Tanuja Bompada, Denis Charles and Eren Manavoglu, A Batch Online Learning Framework for Click Prediction in Search Advertisement, Microsoft Journal of Applied Research, 7th Volume, Spring 2017

  18. Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, Jeff Bilmes, SVitchboard-II and FiSVer-I: Crafting high quality and low complexity conversational english speech corpora using submodular function optimization, Computer Speech & Language 42, 122-142, 2017 (Corpus Definitions and Baselines for SVitchboard-II and FiSVer-I datasets can be found at this link)

  19. Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes, Algorithms for optimizing the ratio of submodular functions, In Proc. International Conference on Machine Learning( ICML) 2016 (Link to Video)

  20. Kai Wei, Rishabh Iyer, Shenjie Wang, Wenruo Bai, Jeff Bilmes, Mixed robust/average submodular partitioning: Fast algorithms, guarantees, and applications, In Advances of Neural Information Processing Systems (NIPS) 2015

  21. Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A Bilmes, Submodular hamming metrics, In Advances in Neural Information Processing Systems 2015

  22. Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, Jeff Bilmes, SVitchboard II and FiSVer I: High-Quality Limited-Complexity Corpora of Conversational English Speech, In Proc. Interspeech, 2015 (Corpus Definitions and Baselines for SVitchboard-II and FiSVer-I datasets can be found at this link)

  23. Ramkrishna Bairi, Rishabh Iyer, Ganesh Ramakrishnan, Jeff Bilmes, Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures, In Association of Computational Linguists (ACL) 2015

  24. Rishabh Iyer and Jeff Bilmes, Submodular point processes with applications to machine learning, Proc. Artificial Intelligence and Statistics (AISTATS) 2015

  25. Yoshinobu Kawahara, Rishabh Iyer, Jeffrey Bilmes, On Approximate Non-submodular Minimization via Tree-Structured Supermodularity, Artificial Intelligence and Statistics (AISTATS) 2015

  26. Kai Wei, Rishabh Iyer, Jeff Bilmes, Submodularity in data subset selection and active learning, International Conference on Machine Learning (ICML) 2015

  27. Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, Jeff A Bilmes, Learning mixtures of submodular functions for image collection summarization, In Advances in Neural Information Processing Systems (NIPS) 2014

  28. Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes, Fast multi-stage submodular maximization, International Conference on Machine Learning (ICML 2014) )

  29. Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes, Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization, Uncertainty in Artificial Intelligence (UAI) 2014

  30. Rishabh Iyer, Rushikesh Borse, Subhasis Chaudhuri, Embedding capacity estimation of reversible watermarking schemes, Springer, Sadhana 39 (6), 1357-1385, October 2014

  31. Rishabh Iyer and Jeff Bilmes, Submodular optimization with submodular cover and submodular knapsack constraints, In Advances Neural Information Processing Systems 2013 (Winner of the Outstanding Paper Award) Link to Video, from 56th Minute.

  32. Rishabh K Iyer, Stefanie Jegelka, Jeff A Bilmes, Curvature and optimal algorithms for learning and minimizing submodular functions, In Advances of Neural Information Processing Systems 2013

  33. Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes, Fast semidifferential-based submodular function optimization, International Conference on Machine Learning (ICML) 2013 (Winner of the Best Paper Award)

  34. Rishabh Iyer, Jeff A Bilmes, The Lovász-Bregman Divergence and connections to rank aggregation, clustering, and web ranking, Uncertainty In Artificial Intelligence (UAI) 2013

  35. Rishabh Iyer, Jeff A Bilmes, Submodular-Bregman and the Lovász-Bregman divergences with applications, In Advances in Neural Information Processing Systems 2012

  36. Rishabh Iyer, Jeff Bilmes, Algorithms for approximate minimization of the difference between submodular functions, with applications, Uncertainty in Artificial Intelligence (UAI) 2012

  37. Ronak Shah, Rishabh Iyer, Subhasis Chaudhuri, Object mining for large video data, British Machine Vision Conference (BMVC) 2011

Patents

  1. D.M Chickering, Christopher A. Meek, Patrice Y. Simard, Rishabh Iyer, Active Machine Learning, US Patent Granted 2019 (US20160162802A1, Application No: US14/562,747)

  2. S. Chaudhuri, Ronak Shah,Rishabh Iyer, Sunita Sarawagi, Visual Storyboard Construction from Scripts and Subtitles, Indian patent No. 321522 (Granted: 2019, Application no. 2818/MUM/2011)

Software

  1. Rishabh Iyer, John Halloran and Kai Wei, Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization, arXiv preprint arXiv:1807.06574 (GitHub Repository)

Workshop Papers

  1. Savan Amitbhai Visalpara (The University of Texas at Dallas)*; Krishnateja Killamsetty (The University of Texas at Dallas); Rishabh Iyer (The University of Texas at Dallas), A Data Subset Selection Framework for Efficient Hyper-Parameter Tuning and Automatic Machine Learning, Workshop on Subset Selection in Machine Learning, SubSetML 2021, In Conjunction with ICML 2021

  2. Krishnateja Killamsetty, Durga Sivasubramanian, Baharan Mirzasoleiman, Ganesh Ramakrishnan, Abir De, Rishabh Iyer, A Gradient Matching Framework for Efficient Learning, Workshop on Hardware Aware Efficient Training, In Conjunction with ICLR 2021

  3. Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer, Submodular Mutual Information for Targeted Data Subset Selection, From Shallow to Deep: Overcoming Limited and Adverse Data Workshop, In Conjunction with ICLR 2021

  4. V. Kaushal, S. Kothawade, R. Iyer and G. Ramakrishnan, Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework, ACMM Workshops 2020​ Link to the Dataset

  5. Saiteja Nalla, Mohit Agrawal, Vishal Kaushal, Ganesh Ramakrishnan and Rishabh Iyer, Watch Hours in Minutes: Summarizing Videos with User Intent, Video Turing Test: Toward Human-Level Video Story Understanding, ECCV Workshop 2020

  6. Srijita Das, Rishabh Iyer and Sriraam Natarajan, Cost Aware Feature Elicitation, International Workshop on Knowledge-infused Mining and Learning (KIML) 2020, Organized In conjunction with 26th ACM Conference on Knowledge Discovery and Data Mining (KDD 2020)

  7. Kai Wei, Rishabh Iyer, Shenjie Wang, Wenruo Bai, Jeff Bilmes, How to intelligently distribute training data to multiple compute nodes: Distributed machine learning via submodular partitioning, Neural Information Processing Society (NIPS) Workshop, Montreal, Canada 2015

  8. Kai Wei, Rishabh Iyer, Shenjie Wang, Wenruo Bai, Jeff Bilmes, Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation, Neural Information Processing Society (NIPS) Workshop, Montreal, Canada 2015

  9. Rishabh Iyer, Jeff Bilmes, Near Optimal algorithms for constrained submodular programs with discounted cooperative costs, NIPS Workshop on Discrete Optimization in Machine Learning (DISCML) 2014

  10. Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes, Mirror descent like algorithms for submodular optimization, NIPS Workshop on Discrete Optimization in Machine Learning (DISCML) 2012

  11. Rishabh Iyer and Torsten Moller, A spatial domain optimization of sampling point-set, MITACS Globalink Research Symposium 2010.

Tutorials and Summer Schools

  1. Rishabh Iyer and Ganesh Ramakrishnan, Combinatorial Approaches for Data, Feature and Topic Selection and Summarization, Tutorial at IJCAI-PRICAI 2020 (See Video)

  2. Rishabh Iyer and Ganesh Ramakrishnan, Submodular Approaches for Data, Feature and Topic Selection and Summarization, Tutorial at ECAI 2020

  3. Rishabh Iyer, Combinatorial Approaches for Visual Data Summarization, Tutorial at WACV 2019

  4. Rishabh Iyer, Submodular Optimization and Data Summarization with Applications to Computer Vision, AMS Sectional Meeting, Special Session on Geometry and Optimization in Computer Vision, Pullman, WA, March 2017

  5. Rishabh Iyer, Submodularity, Convexity and Concavity, International Symposium on Mathematical Programming (ISMP), Pittsburg - July, 2015

  6. Rishabh Iyer and Jeff Bilmes, Submodular Optimization in Machine Learning, Non Convex Optimization in Machine Learning (NOML) Summer School, IIT Bombay, 2015