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Rishabh K. Iyer
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- affiliation: University of Texas at Dallas, TX, USA
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2020 – today
- 2024
- [c59]Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh K. Iyer, Ganesh Ramakrishnan:
Gradient Coreset for Federated Learning. WACV 2024: 2636-2645 - [c58]Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade, Rishabh K. Iyer:
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification. WACV 2024: 2869-2877 - [i66]Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh K. Iyer, Ganesh Ramakrishnan:
Gradient Coreset for Federated Learning. CoRR abs/2401.06989 (2024) - [i65]Nathan Beck, Truong Pham, Rishabh K. Iyer:
Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection. CoRR abs/2402.13454 (2024) - [i64]Nathan Beck, Adithya Iyer, Rishabh K. Iyer:
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning. CoRR abs/2402.13468 (2024) - [i63]Anay Majee, Ryan Sharp, Rishabh K. Iyer:
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection. CoRR abs/2407.02665 (2024) - 2023
- [c57]Suraj Kothawade, Anmol Reddy Mekala, D. Chandra Sekhara Hetha Havya, Mayank Kothyari, Rishabh K. Iyer, Ganesh Ramakrishnan, Preethi Jyothi:
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation. ACL (1) 2023: 5810-5822 - [c56]H. S. V. N. S. Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh K. Iyer, Balaji Krishnamurthy:
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models. EMNLP (Findings) 2023: 6690-6705 - [c55]Parth Vipul Sangani, Arjun Shashank Kashettiwar, Pritish Chakraborty, Bhuvan Reddy Gangula, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh K. Iyer, Abir De:
Discrete Continuous Optimization Framework for Simultaneous Clustering and Training in Mixture Models. ICML 2023: 29950-29970 - [c54]Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh K. Iyer, Abir De:
Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks. NeurIPS 2023 - [i62]KrishnaTeja Killamsetty, Alexandre V. Evfimievski, Tejaswini Pedapati, Kiran Kate, Lucian Popa, Rishabh K. Iyer:
MILO: Model-Agnostic Subset Selection Framework for Efficient Model Training and Tuning. CoRR abs/2301.13287 (2023) - [i61]H. S. V. N. S. Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh K. Iyer, Balaji Krishnamurthy:
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Large Language Models. CoRR abs/2305.06677 (2023) - [i60]Nathan Beck, Suraj Kothawade, Pradeep Shenoy, Rishabh K. Iyer:
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional Settings. CoRR abs/2305.10643 (2023) - [i59]Nathan Beck, Krishnateja Killamsetty, Suraj Kothawade, Rishabh K. Iyer:
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification. CoRR abs/2306.01277 (2023) - [i58]Anay Majee, Suraj Kothawade, Krishnateja Killamsetty, Rishabh K. Iyer:
SCoRe: Submodular Combinatorial Representation Learning for Real-World Class-Imbalanced Settings. CoRR abs/2310.00165 (2023) - 2022
- [j2]Rishabh K. Iyer
, Ninad Khargonkar
, Jeff A. Bilmes, Himanshu Asnani:
Generalized Submodular Information Measures: Theoretical Properties, Examples, Optimization Algorithms, and Applications. IEEE Trans. Inf. Theory 68(2): 752-781 (2022) - [c53]KrishnaTeja Killamsetty, Changbin Li, Chen Zhao, Feng Chen, Rishabh K. Iyer:
A Nested Bi-level Optimization Framework for Robust Few Shot Learning. AAAI 2022: 7176-7184 - [c52]Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff A. Bilmes, Rishabh K. Iyer:
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection. AAAI 2022: 10238-10246 - [c51]Ayush Maheshwari, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh K. Iyer, Marina Danilevsky, Lucian Popa:
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming. ACL (Findings) 2022: 1188-1202 - [c50]Rishabh Tiwari, KrishnaTeja Killamsetty, Rishabh K. Iyer, Pradeep Shenoy:
GCR: Gradient Coreset based Replay Buffer Selection for Continual Learning. CVPR 2022: 99-108 - [c49]Suraj Kothawade
, Saikat Ghosh, Sumit Shekhar
, Yu Xiang
, Rishabh K. Iyer
:
Talisman: Targeted Active Learning for Object Detection with Rare Classes and Slices Using Submodular Mutual Information. ECCV (38) 2022: 1-16 - [c48]Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh K. Iyer:
SPEAR : Semi-supervised Data Programming in Python. EMNLP (Demos) 2022: 121-127 - [c47]Ashish R. Mittal, Durga Sivasubramanian, Rishabh K. Iyer, Preethi Jyothi, Ganesh Ramakrishnan:
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training. EMNLP (Findings) 2022: 5999-6010 - [c46]Xujiang Zhao, KrishnaTeja Killamsetty, Rishabh K. Iyer, Feng Chen:
How Out-of-Distribution Data Hurts Semi-Supervised Learning. ICDM 2022: 763-772 - [c45]Changbin Li, Suraj Kothawade, Feng Chen, Rishabh K. Iyer:
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information. ICML 2022: 12826-12842 - [c44]Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Ganesh Ramakrishnan, Rishabh K. Iyer:
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification. MILLanD@MICCAI 2022: 119-129 - [c43]Suraj Kothawade, Akshit Shrivastava
, Venkat Iyer, Ganesh Ramakrishnan, Rishabh K. Iyer:
DIAGNOSE: Avoiding Out-of-Distribution Data Using Submodular Information Measures. MILLanD@MICCAI 2022: 141-150 - [c42]Athresh Karanam, KrishnaTeja Killamsetty, Harsha Kokel, Rishabh K. Iyer:
ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift. NeurIPS 2022 - [c41]KrishnaTeja Killamsetty, Guttu Sai Abhishek, Aakriti, Ganesh Ramakrishnan, Alexandre V. Evfimievski, Lucian Popa, Rishabh K. Iyer:
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning. NeurIPS 2022 - [i57]Changbin Li, Suraj Kothawade, Feng Chen, Rishabh K. Iyer:
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information. CoRR abs/2201.12928 (2022) - [i56]Vishal Kaushal, Ganesh Ramakrishnan, Rishabh K. Iyer:
Submodlib: A Submodular Optimization Library. CoRR abs/2202.10680 (2022) - [i55]Suraj Kothawade, Pavan Kumar Reddy, Ganesh Ramakrishnan, Rishabh K. Iyer:
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets. CoRR abs/2203.05651 (2022) - [i54]KrishnaTeja Killamsetty, Guttu Sai Abhishek, Aakriti, Alexandre V. Evfimievski, Lucian Popa, Ganesh Ramakrishnan, Rishabh K. Iyer:
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning. CoRR abs/2203.08212 (2022) - [i53]Suraj Kothawade, Shivang Chopra, Saikat Ghosh, Rishabh K. Iyer:
Active Data Discovery: Mining Unknown Data using Submodular Information Measures. CoRR abs/2206.08566 (2022) - [i52]Suraj Kothawade, Atharv Savarkar, Venkat Iyer, Lakshman Tamil, Ganesh Ramakrishnan, Rishabh K. Iyer:
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification. CoRR abs/2210.01520 (2022) - [i51]Suraj Kothawade, Akshit Srivastava, Venkat Iyer, Ganesh Ramakrishnan, Rishabh K. Iyer:
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures. CoRR abs/2210.01526 (2022) - [i50]Ashish R. Mittal, Durga Sivasubramanian, Rishabh K. Iyer, Preethi Jyothi, Ganesh Ramakrishnan:
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training. CoRR abs/2210.16892 (2022) - 2021
- [c40]KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh K. Iyer:
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning. AAAI 2021: 8110-8118 - [c39]Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh K. Iyer:
Semi-Supervised Data Programming with Subset Selection. ACL/IJCNLP (Findings) 2021: 4640-4651 - [c38]Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh K. Iyer:
Rule Augmented Unsupervised Constituency Parsing. ACL/IJCNLP (Findings) 2021: 4923-4932 - [c37]Rishabh K. Iyer, Ninad Khargoankar, Jeff A. Bilmes, Himanshu Asanani:
Submodular combinatorial information measures with applications in machine learning. ALT 2021: 722-754 - [c36]Srijita Das, Rishabh K. Iyer, Sriraam Natarajan:
A Clustering based Selection Framework for Cost Aware and Test-time Feature Elicitation. COMAD/CODS 2021: 20-28 - [c35]KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh K. Iyer:
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training. ICML 2021: 5464-5474 - [c34]Durga Sivasubramanian, Rishabh K. Iyer, Ganesh Ramakrishnan, Abir De:
Training Data Subset Selection for Regression with Controlled Generalization Error. ICML 2021: 9202-9212 - [c33]Himanshu Asnani, Jeff A. Bilmes, Rishabh K. Iyer:
Independence Properties of Generalized Submodular Information Measures. ISIT 2021: 999-1004 - [c32]Ping Zhang, Rishabh K. Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De:
Learning to Select Exogenous Events for Marked Temporal Point Process. NeurIPS 2021: 347-361 - [c31]KrishnaTeja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh K. Iyer:
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning. NeurIPS 2021: 14488-14501 - [c30]Suraj Kothawade, Nathan Beck, KrishnaTeja Killamsetty, Rishabh K. Iyer:
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios. NeurIPS 2021: 18685-18697 - [c29]Chandrashekhar Lavania, Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
A Practical Online Framework for Extracting Running Video Summaries under a Fixed Memory Budget. SDM 2021: 226-234 - [i49]Vishal Kaushal, Suraj Kothawade, Anshul Tomar, Rishabh K. Iyer, Ganesh Ramakrishnan:
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization. CoRR abs/2101.10514 (2021) - [i48]KrishnaTeja Killamsetty, Durga Sivasubramanian, Baharan Mirzasoleiman, Ganesh Ramakrishnan, Abir De, Rishabh K. Iyer:
GRAD-MATCH: A Gradient Matching Based Data Subset Selection for Efficient Learning. CoRR abs/2103.00123 (2021) - [i47]Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff A. Bilmes, Rishabh K. Iyer:
PRISM: A Unified Framework of Parameterized Submodular Information Measures for Targeted Data Subset Selection and Summarization. CoRR abs/2103.00128 (2021) - [i46]Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff A. Bilmes, Rishabh K. Iyer:
Submodular Mutual Information for Targeted Data Subset Selection. CoRR abs/2105.00043 (2021) - [i45]Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh K. Iyer:
Rule Augmented Unsupervised Constituency Parsing. CoRR abs/2105.10193 (2021) - [i44]KrishnaTeja Killamsetty, Xujiang Zhao, Feng Chen, Rishabh K. Iyer:
RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning. CoRR abs/2106.07760 (2021) - [i43]Durga Sivasubramanian, Rishabh K. Iyer, Ganesh Ramakrishnan, Abir De:
Training Data Subset Selection for Regression with Controlled Generalization Error. CoRR abs/2106.12491 (2021) - [i42]Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, Rishabh K. Iyer:
Effective Evaluation of Deep Active Learning on Image Classification Tasks. CoRR abs/2106.15324 (2021) - [i41]Suraj Kothawade, Nathan Beck, KrishnaTeja Killamsetty, Rishabh K. Iyer:
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios. CoRR abs/2107.00717 (2021) - [i40]Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh K. Iyer:
SPEAR : Semi-supervised Data Programming in Python. CoRR abs/2108.00373 (2021) - [i39]Himanshu Asnani, Jeff A. Bilmes, Rishabh K. Iyer:
Independence Properties of Generalized Submodular Information Measures. CoRR abs/2108.03154 (2021) - [i38]Ayush Maheshwari, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh K. Iyer, Marina Danilevsky, Lucian Popa:
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming. CoRR abs/2109.11410 (2021) - [i37]Mayank Kothyari, Anmol Reddy Mekala, Rishabh K. Iyer, Ganesh Ramakrishnan, Preethi Jyothi:
Personalizing ASR with limited data using targeted subset selection. CoRR abs/2110.04908 (2021) - [i36]Rishabh Tiwari, KrishnaTeja Killamsetty, Rishabh K. Iyer, Pradeep Shenoy:
GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning. CoRR abs/2111.11210 (2021) - [i35]Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang, Rishabh K. Iyer:
TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information. CoRR abs/2112.00166 (2021) - 2020
- [c28]Rishabh K. Iyer:
Robust Submodular Minimization with Applications to Cooperative Modeling. ECAI 2020: 451-458 - [c27]Saiteja Nalla, Mohit Agrawal, Vishal Kaushal, Ganesh Ramakrishnan, Rishabh K. Iyer:
Watch Hours in Minutes: Summarizing Videos with User Intent. ECCV Workshops (5) 2020: 714-730 - [c26]Rishabh K. Iyer, Jeff A. Bilmes:
Concave Aspects of Submodular Functions. ISIT 2020: 72-77 - [c25]Srijita Das, Rishabh K. Iyer, Sriraam Natarajan:
Cost Aware Feature Elicitation. KiML@KDD 2020: 23-29 - [c24]Vishal Kaushal, Suraj Kothawade, Rishabh K. Iyer, Ganesh Ramakrishnan:
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework. AI4TV@MM 2020: 37-44 - [i34]Rishabh K. Iyer:
Robust Submodular Minimization with Applications to Cooperative Modeling. CoRR abs/2001.09360 (2020) - [i33]Rishabh K. Iyer, Ninad Khargoankar, Jeff A. Bilmes, Himanshu Asanani:
Submodular Combinatorial Information Measures with Applications in Machine Learning. CoRR abs/2006.15412 (2020) - [i32]Rishabh K. Iyer, Jeff A. Bilmes:
Concave Aspects of Submodular Functions. CoRR abs/2006.16784 (2020) - [i31]Vishal Kaushal, Suraj Kothawade, Rishabh K. Iyer, Ganesh Ramakrishnan:
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework. CoRR abs/2007.14560 (2020) - [i30]Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Rishabh K. Iyer, Ganesh Ramakrishnan:
Data Programming using Semi-Supervision and Subset Selection. CoRR abs/2008.09887 (2020) - [i29]Xujiang Zhao, KrishnaTeja Killamsetty, Rishabh K. Iyer, Feng Chen:
Robust Semi-Supervised Learning with Out of Distribution Data. CoRR abs/2010.03658 (2020) - [i28]Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff A. Bilmes, Himanshu Asnani, Rishabh K. Iyer:
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures. CoRR abs/2010.05631 (2020) - [i27]Suraj Kothawade, Jiten Girdhar, Chandrashekhar Lavania, Rishabh K. Iyer:
Deep Submodular Networks for Extractive Data Summarization. CoRR abs/2010.08593 (2020) - [i26]KrishnaTeja Killamsetty, Changbin Li, Chen Zhao, Rishabh K. Iyer, Feng Chen:
A Reweighted Meta Learning Framework for Robust Few Shot Learning. CoRR abs/2011.06782 (2020) - [i25]KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh K. Iyer:
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning. CoRR abs/2012.10630 (2020)
2010 – 2019
- 2019
- [c23]Rishabh K. Iyer, Jeffrey A. Bilmes:
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs. AISTATS 2019: 276-285 - [c22]Rishabh K. Iyer, Jeffrey A. Bilmes:
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems. AISTATS 2019: 2340-2349 - [c21]Vishal Kaushal, Rishabh K. Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan:
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance. WACV 2019: 452-461 - [c20]Vishal Kaushal, Sandeep Subramanian, Suraj Kothawade, Rishabh K. Iyer, Ganesh Ramakrishnan:
A Framework Towards Domain Specific Video Summarization. WACV 2019: 666-675 - [c19]Vishal Kaushal, Rishabh K. Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan:
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision. WACV 2019: 1289-1299 - [i24]Vishal Kaushal, Rishabh K. Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan:
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision. CoRR abs/1901.01151 (2019) - [i23]Vishal Kaushal, Rishabh K. Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan:
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity, Representation, Coverage and Importance. CoRR abs/1901.01153 (2019) - [i22]Rishabh K. Iyer, Jeff A. Bilmes:
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs. CoRR abs/1902.10172 (2019) - [i21]Rishabh K. Iyer, Jeff A. Bilmes:
A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems. CoRR abs/1902.10176 (2019) - [i20]Rishabh K. Iyer:
A Unified Framework of Robust Submodular Optimization. CoRR abs/1906.06393 (2019) - 2018
- [i19]John Moore, Joel Pfeiffer, Kai Wei, Rishabh K. Iyer, Denis Charles, Ran Gilad-Bachrach, Levi Boyles, Eren Manavoglu:
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks. CoRR abs/1804.06909 (2018) - [i18]Pratik Dubal, Rohan Mahadev, Suraj Kothawade, Kunal Dargan, Rishabh K. Iyer:
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras. CoRR abs/1805.10604 (2018) - [i17]Vishal Kaushal, Anurag Sahoo, Khoshrav Doctor, Narasimha Raju Uppalapati, Suyash Shetty, Pankaj Singh, Rishabh K. Iyer, Ganesh Ramakrishnan:
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks. CoRR abs/1805.11191 (2018) - [i16]Rishabh K. Iyer, John T. Halloran, Kai Wei:
Jensen: An Easily-Extensible C++ Toolkit for Production-Level Machine Learning and Convex Optimization. CoRR abs/1807.06574 (2018) - [i15]Rishabh K. Iyer, Nimit Acharya, Tanuja Bompada, Denis Charles, Eren Manavoglu:
A Unified Batch Online Learning Framework for Click Prediction. CoRR abs/1809.04673 (2018) - [i14]Rishabh K. Iyer, Pratik Dubal, Kunal Dargan, Suraj Kothawade, Rohan Mahadev, Vishal Kaushal:
Vis-DSS: An Open-Source toolkit for Visual Data Selection and Summarization. CoRR abs/1809.08846 (2018) - [i13]Vishal Kaushal, Rishabh K. Iyer, Suraj Kothawade, Sandeep Subramanian, Ganesh Ramakrishnan:
A Framework towards Domain Specific Video Summarization. CoRR abs/1809.08854 (2018) - 2017
- [j1]Yuzong Liu, Rishabh K. Iyer, Katrin Kirchhoff, Jeff A. Bilmes:
SVitchboard-II and FiSVer-I: Crafting high quality and low complexity conversational english speech corpora using submodular function optimization. Comput. Speech Lang. 42: 122-142 (2017) - [i12]Anurag Sahoo, Vishal Kaushal, Khoshrav Doctor, Suyash Shetty, Rishabh K. Iyer, Ganesh Ramakrishnan:
A Unified Multi-Faceted Video Summarization System. CoRR abs/1704.01466 (2017) - 2016
- [c18]Wenruo Bai, Rishabh K. Iyer, Kai Wei, Jeff A. Bilmes:
Algorithms for Optimizing the Ratio of Submodular Functions. ICML 2016: 2751-2759 - 2015
- [c17]Ramakrishna Bairi, Rishabh K. Iyer, Ganesh Ramakrishnan, Jeff A. Bilmes:
Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures. ACL (1) 2015: 553-563 - [c16]Rishabh K. Iyer, Jeff A. Bilmes:
Submodular Point Processes with Applications to Machine learning. AISTATS 2015 - [c15]Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes:
On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. AISTATS 2015 - [c14]Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
Submodularity in Data Subset Selection and Active Learning. ICML 2015: 1954-1963 - [c13]Yuzong Liu, Rishabh K. Iyer, Katrin Kirchhoff, Jeff A. Bilmes:
SVitchboard II and fiSVer i: high-quality limited-complexity corpora of conversational English speech. INTERSPEECH 2015: 673-677 - [c12]Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes:
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications. NIPS 2015: 2233-2241 - [c11]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. NIPS 2015: 3141-3149 - [i11]Rishabh K. Iyer, Jeff A. Bilmes:
Polyhedral aspects of Submodularity, Convexity and Concavity. CoRR abs/1506.07329 (2015) - [i10]Kai Wei, Rishabh K. Iyer, Shengjie Wang, Wenruo Bai, Jeff A. Bilmes:
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation. CoRR abs/1510.08865 (2015) - [i9]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. CoRR abs/1511.02163 (2015) - 2014
- [c10]Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
Fast Multi-stage Submodular Maximization. ICML 2014: 1494-1502 - [c9]Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes:
Learning Mixtures of Submodular Functions for Image Collection Summarization. NIPS 2014: 1413-1421 - [c8]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization. UAI 2014: 360-369 - [i8]Rishabh K. Iyer, Jeff A. Bilmes:
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications. CoRR abs/1408.2051 (2014) - [i7]Rishabh K. Iyer, Jeff A. Bilmes:
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking. CoRR abs/1408.2062 (2014) - 2013
- [c7]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Fast Semidifferential-based Submodular Function Optimization. ICML (3) 2013: 855-863 - [c6]Rishabh K. Iyer, Jeff A. Bilmes:
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints. NIPS 2013: 2436-2444 - [c5]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. NIPS 2013: 2742-2750 - [c4]Rishabh K. Iyer, Jeff A. Bilmes:
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking. UAI 2013 - [i6]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Fast Semidifferential-based Submodular Function Optimization. CoRR abs/1308.1006 (2013) - [i5]Rishabh K. Iyer, Jeff A. Bilmes:
The Lovasz-Bregman Divergence and connections to rank aggregation, clustering, and web ranking. CoRR abs/1308.5275 (2013) - [i4]Rishabh K. Iyer, Jeff A. Bilmes:
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints. CoRR abs/1311.2106 (2013) - [i3]Rishabh K. Iyer, Stefanie Jegelka, Jeff A. Bilmes:
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions. CoRR abs/1311.2110 (2013) - 2012
- [c3]Rishabh K. Iyer, Jeff A. Bilmes:
Submodular-Bregman and the Lovász-Bregman Divergences with Applications. NIPS 2012: 2942-2950 - [c2]Rishabh K. Iyer, Jeff A. Bilmes:
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications. UAI 2012: 407-417 - [i2]Rishabh K. Iyer, Jeff A. Bilmes:
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications. CoRR abs/1207.0560 (2012) - 2011
- [c1]Ronak Shah, Rishabh K. Iyer, Subhasis Chaudhuri:
Object Mining for Large Video data. BMVC 2011: 1-11 - [i1]Rishabh K. Iyer, Rushikesh Borse
, Ronak Shah, Subhasis Chaudhuri:
Estimation of the Embedding Capacity in Pixel-pair based Watermarking Schemes. CoRR abs/1111.5653 (2011)
Coauthor Index
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