Publications
For a complete list of publications from our lab, please visit the following Google Scholar Profile.
* indicates equal contribution
Pre-Prints
Krishnateja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh Iyer. “MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning”. arXiv:2301.13287.
Suraj Kothawade, Shivang Chopra, Saikat Ghosh, and Rishabh Iyer. “Active Data Discovery: Mining Unknown Data using Submodular Information Measures”. arXiv:2206.08566.
Mayank Kothyari, Anmol Reddy Mekala, Rishabh Iyer, Ganesh Ramakrishnan, and Preethi Jyothi. “Personalizing ASR with Limited Data Using Targeted Subset Selection”. arXiv:2110.04908.
Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, and Rishabh Iyer. “Effective Evaluation of Deep Active Learning on Image Classification Tasks”. arXiv:2106.15324.
Suraj Kothawade, Jiten Girdhar, Chandrashekar Lavania, and Rishabh Iyer. “Deep Submodular Networks for Extractive Data Summarization”. arXiv:2010.08593.
MS Ozdayi, M Kantarcioglu, R Iyer. “BiFair: Training Fair Models with Bilevel Optimization”. arXiv:2106.04757.
Conferences & Journals Publications (Since 2019)
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti, Alexandre V. Evfimievski, Lucian Popa, Ganesh Ramakrishnan, Rishabh Iyer, “AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning”. In Neural Information Processing Systems, NeurIPS 2022. (25.6% Acceptance Rate)
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. (25.6% Acceptance Rate)
Xujiang Zhao*, Killamsetty Krishnateja*, Rishabh Iyer, Feng Chen. “How Out of Distribution Data Hurts Semi-Supervised Learning”. In IEEE International Conference on Data Mining, ICDM 2022. (9% Acceptance Rate)
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 European Conference on Computer Vision, ECCV 2022.
Changbin Li*, Suraj Kothawade*, Feng Chen, Rishabh Iyer. “PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information”. In the Thirty-ninth International Conference on Machine Learning , ICML 2022.
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, Pradeep Shenoy, “GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning”. In Conference on Computer Vision and Pattern Recognition, CVPR 2022.
Ayush Maheshwari*, Krishnateja Killamsetty*, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa. “Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming”. In Findings of the Association for Computational Linguistics: ACL 2022. (Long paper)
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 the Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022. (15% Acceptance Rate)
Krishnateja Killamsetty*, Changbin Li*, Chen Zhao, Rishabh Iyer, Feng Chen. A Nested Bi-level Optimization Framework for Robust Few Shot Learning”. In the Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022. (15% Acceptance Rate)
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, 2021
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. (26% Acceptance Rate)
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. (26% Acceptance Rate)
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. (26% Acceptance Rate)
Ayush Maheshwari, Oishik Chatterjee, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer.“Semi-Supervised Data Programming with Subset Selection”. In Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021, ACL/IJCNLP 2021:4640–4651. Findings of ACL. Association for Computational Linguistics, 2021. (Long paper)
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
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer. “GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training”. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, 139:5464–5474. Proceedings of Machine Learning Research. PMLR, 2021. (21% Acceptance Rate)
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer. “GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning”. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Event, February 2-9, 2021, 8110–8118. AAAI Press, 2021. (21% Acceptance Rate)
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)
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
Himanshu Asnani, Jeff Bilmes, and Rishabh Iyer, Independence Properties of Generalized Submodular Information Measures, 2021 IEEE International Symposium on Information Theory, ISIT 2021
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
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)
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)
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
Rishabh Iyer and Jeff Bilmes, A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems, Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan (32.4% Acceptance Rate)
Rishabh Iyer and Jeff Bilmes, Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs, Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan (32.4% Acceptance Rate)
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 . Link to Video
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 Video
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
Selected Older Publications
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 (Conference version of this paper appeared in INTERSPEECVH 2015)
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
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
Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A Bilmes, Submodular hamming metrics, In Advances in Neural Information Processing Systems 2015
Kai Wei, Rishabh Iyer, Jeff Bilmes, Submodularity in data subset selection and active learning, International Conference on Machine Learning (ICML) 2015
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
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)
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)
Rishabh Iyer, Jeff A Bilmes, Submodular-Bregman and the Lovász-Bregman divergences with applications, In Advances in Neural Information Processing Systems 2012
Rishabh Iyer, Jeff Bilmes, Algorithms for approximate minimization of the difference between submodular functions, with applications, Uncertainty in Artificial Intelligence (UAI) 2012
Software
CORDS: Coresets and Data Subset selection
DISTIL: Deep Diversified inter-active Learning
SubmodLib: Library for Submodular Optimization
SPEAR: Semi Supervised Data Programming
TRUST: Targeted Subset Seleciton
Workshop Papers
H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh K Iyer, Balaji Krishnamurthy. “Using Informative Data Subsets for Efficient Training of Large Language Models: An Initial Study”. The Second Workshop on Efficient Natural Language and Speech Processing (ENLSP), In Conjunction with NeurIPS 2022.
Suraj Kothawade, Shivang Chopra, Saikat Ghosh, and Rishabh Iyer. “Active Data Discovery: Mining Unknown Data using Submodular Information Measures”. In ReALML, ICML 2022, Adaptive Experimental Design and Active Learning in the Real World.
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
Krishnateja Killamsetty*, Changbin Li*, Chen Zhao, Rishabh Iyer, Feng Chen. A Nested Bi-level Optimization Framework for Robust Few Shot Learning. Fifth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems, In Conjunction with NeurIPS 2021
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
Suraj Kothawade, Vishal Kaushal, 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
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
Vishal Kaushal, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan. “Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework” ACMM Workshops 2020. Link to the Dataset
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.
Srijita Das, Rishabh Iyer and Sriraam Natarajan. “Cost Aware Feature Elicitation,” nternational Workshop on Knowledge-infused Mining and Learning (KIML) 2020, Organized In conjunction with 26th ACM Conference on Knowledge Discovery and Data Mining (KDD 2020)