default search action
Rishabh K. Iyer
Person information
- affiliation: University of Texas at Dallas, TX, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
- [c61]Anay Majee, Ryan Sharp, Rishabh K. Iyer:
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection. ECCV (32) 2024: 350-366 - [c60]Anay Majee, Suraj Kothawade, Krishnateja Killamsetty, Rishabh K. Iyer:
SCoRe: Submodular Combinatorial Representation Learning. ICML 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 - [i68]Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh K. Iyer, Ganesh Ramakrishnan:
Gradient Coreset for Federated Learning. CoRR abs/2401.06989 (2024) - [i67]Nathan Beck, Truong Pham, Rishabh K. Iyer:
Theoretical Analysis of Submodular Information Measures for Targeted Data Subset Selection. CoRR abs/2402.13454 (2024) - [i66]Nathan Beck, Adithya Iyer, Rishabh K. Iyer:
STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning. CoRR abs/2402.13468 (2024) - [i65]Anay Majee, Ryan Sharp, Rishabh K. Iyer:
SMILe: Leveraging Submodular Mutual Information For Robust Few-Shot Object Detection. CoRR abs/2407.02665 (2024) - [i64]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. CoRR abs/2409.12255 (2024) - [i63]Ruiyu Mao, Sarthak Kumar Maharana, Rishabh K. Iyer, Yunhui Guo:
STONE: A Submodular Optimization Framework for Active 3D Object Detection. CoRR abs/2410.03918 (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
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
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
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
Estimation of the Embedding Capacity in Pixel-pair based Watermarking Schemes. CoRR abs/1111.5653 (2011)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-03 20:30 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: