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. Nathan Beck, Suraj Kothawade, Pradeep Shenoy, Rishabh Iyer, STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings, arXiv preprint arXiv:2305.10643

2. Suraj Kothawade, Shivang Chopra, Saikat Ghosh, Rishabh Iyer, Active Data Discovery: Mining Unknown Data using Submodular Information Measures, arXiv:2206.08566

3. K Killamsetty, AV Evfimievski, T Pedapati, K Kate, L Popa, R Iyer, MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning, arXiv preprint arXiv:2301.13287

Peer-Reviewed Publications (Conferences & Journals)

2024

1. Anay Majee, Ryan Sharp, Rishabh Iyer, SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection, In European Conference on Computer Vision, ECCV 2024

2. Anay Majee, Suraj Kothawade, Krishnateja Killiamsetty, Rishabh Iyer, SCoRe: Submodular Combinatorial Representation Learning, In International Conference of Machine Learning, ICML 2024

3. Durga Sivasubramanian, Lokesh Nagalpatti, Rishabh Iyer, Ganesh Ramakrishnan, Gradient Coreset for Federated Learning, In Winter Conference on Applications of Computer Vision (WACV) 2024

4. Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade, Rishabh Iyer, Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification, In Winter Conference on Applications of Computer Vision (WACV) 2024

2023

5. Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish V. Tendulkar, Rishabh K Iyer, Abir De, Learning to Select a Subset of Training Examples to Generalize Efficient Model Training, In Neural Information Processing Systems, NeurIPS 2023

6. HSVNS Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy, INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models, In Findings of EMNLP 2023 (Long Paper)

7. Suraj N. Kothawade, Anmol Reddy Mekala, D.Chandra Sekhara SS Hetha Havya, Mayank Kothyari, Rishabh K. Iyer, Ganesh Ramakrishnan and Preethi Jyothi, DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation, In Association of Computational Linguists, ACL 2023 (Long Paper)

8. Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga S, Ganesh Ramakrishnan, Rishabh K Iyer, Abir De, Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models, In International Conference of Machine Learning, ICML 2023

2022

9. Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti Lnu, Alexandre V. Evfimievski, Lucian Popa, Ganesh Ramakrishnan, Rishabh K Iyer,  AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning, In Neural Information Processing Systems, NeurIPS 2022

10. Athresh Karanam, Krishnateja Killamsetty, Harsha Kokel, Rishabh K Iyer, Orient: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift, In Neural Information Processing Systems, NeurIPS 2022

11. Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang, Rishabh Iyer, TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information, In Proceedings of European Conference on Computer Vision, ECCV 2022

12. Changbin Li, Suraj Kothawade, Feng Chen, and Rishabh Iyer, PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information, In Proc. International Conference of Machine Learning, ICML 2022 (21% Acceptance Rate)

13. Ashish Mittal, Durga Sivasubramanian, Rishabh Iyer, Preethi Jyothi, and Ganesh Ramakrishnan, Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient and Robust ASR Training, In Findings of EMNLP 2022 (Long Paper)

14. Xujiang Zhao, Krishnateja Killamsetty, Rishabh Iyer, and Feng Chen, How Out-of-Distribution Data Hurts Semi-Supervised Learning, In Proc. International Conference on Data Mining, ICDM 2022 (9% Acceptance Rate for Regular/Full Papers)

15. Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, and Pradeep Shenoy, GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning, In Computer Vision and Pattern Recognition, CVPR 2022

16. Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer, PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection, In 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (15% Acceptance Rate)

17. Krishnateja Killamsetty, Changbin Li, Chen Zhou, Feng Chen, Rishabh Iyer, A Nested Bi-level Optimization Framework for Robust Few Shot Learning, In 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (15% Acceptance Rate)

18. Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa, Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming, Findings of ACL 2022 (Long Paper)

19. Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes, Himanshu Asnani, Generalized Submodular Information Measures: Theoretical Properties, Examples, Optimization, Algorithms, and Applications, In IEEE Transactions of Information Theory, February 2022

2021

20. Suraj Kothawade; Nathan Beck; Krishnateja Killamsetty; Rishabh Iyer, SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios, In Neural Information Processing Systems, NeurIPS 2021

21. Krishnateja Killamsetty, Xujiang Zhou, Feng Chen, and Rishabh Iyer, RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning, In Neural Information Processing Systems, NeurIPS 2021

22. Ping Zhang, Rishabh K Iyer, Ashish V. Tendulkar, Gaurav Aggarwal, Abir De, Learning to Select Exogenous Events for Marked Temporal Point Process, In Neural Information Processing Systems, NeurIPS 2021

23. Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer, GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Deep Model Training, The Thirty-eighth International Conference on Machine Learning, ICML 2021 (21% Acceptance Rate)

24. Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan, and Abir De, Training Data Subset Selection for Regression with Controlled Validation Error, The Thirty-eighth International Conference on Machine Learning, ICML 2021 (21% Acceptance Rate) [Project Page]

25. 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)

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

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

28. 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

29. 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)

30. 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

31. 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)

2020

32. 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) 

33. 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

2019

34. 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)

35. 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)

36. Vishal Kaushal, Sandeep Subramanium, Suraj Kothawade, 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)

37. Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, 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)

38. 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)

2017

39. 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)

2016

40. 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) 

2015

41. 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

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

43. 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)

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

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

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

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

2014

48. 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

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

50. 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

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

2013

52. 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.

53. 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

54. 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)

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

2011 & 2012

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

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

58. 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 (GitHub Links) and Demonstrations

1. Suraj Kothawade and Rishabh Iyer, Data Discovery and Targeted Learning, In European Conference on Computer Vision, ECCV Demo Track 2022

2. Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Rishabh Iyer, Ganesh Ramakrishnan, SPEAR : Semi-supervised Data Programming in Python, 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi (Demo paper) 

3. Suraj Kothawade and Rishabh Iyer. An Efficient Data Exploration Framework for Effective Learning. At 33rd IEEE Intelligent Vehicles Symposium, IV 2022 (Demo Track)

4. Krishnateja Killamsetty, Rishabh Iyer, et al, CORDS: Coresets and Data Subset Selection for Compute-Efficient DNN Training and AutoML | 283 ⭐ | 47 Forks

5. Nathan Beck, Suraj Kothawade, Rishabh Iyer et al, DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning | 130 ⭐  | 24 Forks

6. Rishabh Iyer, John Halloran and Kai Wei, Jensen: A C++ Toolkit for Machine Learning and Convex Optimization | 45 ⭐  | 21 Forks

7. Ayush Maheshwari, Rishabh Iyer, Ganesh Ramakrishnan, et al, SPEAR: Semi-Supervised Data programming for Weak Supervision 96 ⭐  | 19 Forks

8. Vishal Kaushal, Rishabh Iyer, Ganesh Ramakrishnan, et al, SubModLib: Summarize Massive Datasets using Submodular Optimization. 69 ⭐  | 22 Forks 

9. Suraj Kothawade and Rishabh Iyer, TRUST: A Guided Active Learning Framework: Targeting, Filtering, and Discovery | 17 ⭐  | 10 Forks 

Workshop Papers

1. Nathan Beck, Adithya Iyer, Rishabh Iyer, STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning, In Data-Centric Machine Learning (DMLR) Workshop at ICML 2024

2. Suraj Kothawade, Akshit Shrivastava, Venkat Iyer, Ganesh Ramakrishnan, Rishabh Iyer, DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures, In Proceedings of the First International Workshop, Medical Image Learning with Limited and Noisy Data 2022, Held in Conjunction with MICCAI 2022, Singapore, September 2022

3. Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Ganesh Ramakrishnan, Rishabh Iyer, CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification, In Proceedings of the First International Workshop, Medical Image Learning with Limited and Noisy Data 2022, Held in Conjunction with MICCAI 2022, Singapore, September, 2022

4. Suraj Kothawade, Shivang Chopra, Saikat Ghosh, Rishabh Iyer, Active Data Discovery: Mining Unknown Data using Submodular Information Measures, In the Adaptive Experimental Design and Active Learning in the Real World, RealML Workshop at ICML 2022

5. Vishak Prasad C, Colin White, Paarth Jain, Sibasis Nayak, Rishabh K Iyer, Ganesh Ramakrishnan, Speeding up NAS with Adaptive Subset Selection, AutoML 2022 Workshop

6. MS Ozdayi, M Kantarcioglu, R Iyer, Fair Machine Learning under Limited Demographically Labeled Data, ICLR Workshop on Socially Responsible Machine Learning

7. Suraj Kothawade, Lakshman Tamil, Rishabh Iyer, Targeted Active Learning using Submodular Mutual Information for Imbalanced Medical Image Classification, Medical Imaging Meets NeurIPS 2021 Workshop in Conjunction with NeurIPS 2021

8. Krishnateja Killamsetty, Changbin Li, Chen Zhou, Rishabh Iyer, and Feng Chen, A Reweighted Meta Learning Framework for Robust Few Shot Learning, NeurIPS 2021 Workshop MetaLearn

9. Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, and Rishabh Iyer, Effective Evaluation of Deep Active Learning on Image Classification Tasks, Workshop on Subset Selection in Machine Learning, SubSetML 2021, In Conjunction with ICML 2021

10. Savan Amitbhai Visalpara; Krishnateja Killamsetty; Rishabh Iyer, 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

11. 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

12. 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

13. 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

14. 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

15. 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)

16. 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

17. 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

18. 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

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

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

Tutorials and Summer Schools

1. Krishnateja Killamsetty, Suraj Kothawade, Nathan Beck, Rishabh Iyer, Subset Selection in Machine Learning: Hands-On Application with CORDS, DISTIL, SUBMODLIB, and TRUST. Lab to be held at AAAI 2023

2. Rishabh Iyer, Abir De, Jeff Bilmes and Ganesh Ramakrishnan, Subset Selection in Machine Learning: Theory, Applications, and Hands-On, Tutorial at AAAI 2022

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

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

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

6. 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 

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

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