Rishabh Iyer

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Jeffrey A. BilmesProfessor of Electrical Engineering and Computer Science, University of Washington SeattleVerified email at uw.edu
Ganesh RamakrishnanProfessor, Department of Computer Science and Engineering, Indian Institute of Technology BombayVerified email at cse.iitb.ac.in
Krishnateja KillamsettyResearch Scientist, IBM ResearchVerified email at ibm.com
Suraj KothawadeUniversity of Texas at DallasVerified email at utdallas.edu
Kai WeiGraduate student of Electrical Engineering, University of WashingtonVerified email at uw.edu
Durga SivasubramanianResearch Scholar, Computer Science and Engineering, Indian Institute of Technology BombayVerified email at cse.iitb.ac.in
Stefanie JegelkaMITVerified email at mit.edu
Abir DeAssistant Professor, CSE, IIT BombayVerified email at cse.iitb.ac.in
Feng ChenDepartment of Computer Science, UT DallasVerified email at utdallas.edu
Ayush MaheshwariPhD Student at CSE, IIT BombayVerified email at cse.iitb.ac.in
Xujiang ZhaoResearcher at NEC Laboratories AmericaVerified email at nec-labs.com
Sebastian TschiatschekUniversity of ViennaVerified email at univie.ac.at
Shengjie WangNew York University ShanghaiVerified email at nyu.edu
Nathan BeckPhD Student of Computer Science, University of Texas, DallasVerified email at utdallas.edu
Lucian PopaIBM Almaden Research CenterVerified email at us.ibm.com
Murat KantarciogluProfessor of Computer Science, University of Texas at DallasVerified email at utdallas.edu
Mustafa Safa OzdayiUT DallasVerified email at utdallas.edu
Preethi JyothiAssociate Professor of Computer Science and Engineering, Indian Institute of Technology BombayVerified email at cse.iitb.ac.in
Pradeep ShenoyUniversity of Washington, University of California, San Diego, Microsoft, Google ResearchVerified email at cs.washington.edu
Himanshu AsnaniAuthor, Translator, ResearcherVerified email at tifr.res.in

Rishabh Iyer

University of Texas Dallas, Microsoft, UW

Verified email at utdallas.edu - Homepage

Machine Learning Combinatorial Optimization Computer Vision Active Learning Efficient & Robust Machine Learning


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Submodularity in data subset selection and active learning K Wei, R Iyer, J Bilmes International conference on machine learning, 1954-1963, 2015	369	2015
Submodular optimization with submodular cover and submodular knapsack constraints RK Iyer, JA Bilmes Advances in Neural Information Processing Systems (NIPS), 2436-2444, 2013	274	2013
Learning mixtures of submodular functions for image collection summarization S Tschiatschek, RK Iyer, H Wei, JA Bilmes Advances in Neural Information Processing Systems (NIPS), 1413-1421, 2014	214	2014
Algorithms for approximate minimization of the difference between submodular functions, with applications R Iyer, J Bilmes Uncertainty in Artificial Intelligence (UAI), 2012	173	2012
Fast semidifferential-based submodular function optimization R Iyer, S Jegelka, J Bilmes International Conference on Machine Learning (ICML), 2013	134	2013
Glister: A generalization based data selection framework for efficient and robust learning K Killamsetty, D Subramanian, G Ramakrishnan, R Iyer AAAI, 2021	113*	2021
Curvature and optimal algorithms for learning and minimizing submodular functions RK Iyer, S Jegelka, JA Bilmes Advances in Neural Information Processing Systems (NIPS), 2742-2750, 2013	113	2013
Grad-match: Gradient matching based data subset selection for efficient deep model training K Killamsetty, S Durga, G Ramakrishnan, A De, R Iyer International Conference on Machine Learning, 5464-5474, 2021	100	2021
Fast multi-stage submodular maximization K Wei, R Iyer, J Bilmes International Conference on Machine Learning (ICML-14), 1494-1502, 2014	97	2014
Learning from less data: A unified data subset selection and active learning framework for computer vision V Kaushal, R Iyer, S Kothawade, R Mahadev, K Doctor, G Ramakrishnan 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1289-1299, 2019	74*	2019
Similar: Submodular information measures based active learning in realistic scenarios S Kothawade, N Beck, K Killamsetty, R Iyer Advances in Neural Information Processing Systems 34, 18685-18697, 2021	55	2021
Submodular combinatorial information measures with applications in machine learning R Iyer, N Khargoankar, J Bilmes, H Asanani Algorithmic Learning Theory, 722-754, 2021	53	2021
Submodular-Bregman and the Lovasz-Bregman Divergences with Applications R Iyer, J Bilmes Advances in Neural Information Processing Systems (NIPS), 2942-2950, 2012	52	2012
Algorithms for optimizing the ratio of submodular functions W Bai, R Iyer, K Wei, J Bilmes International Conference on Machine Learning, 2751-2759, 2016	42	2016
Mixed robust/average submodular partitioning: Fast algorithms, guarantees, and applications K Wei, RK Iyer, S Wang, W Bai, JA Bilmes Advances in Neural Information Processing Systems 28, 2015	41	2015
Retrieve: Coreset selection for efficient and robust semi-supervised learning K Killamsetty, X Zhao, F Chen, R Iyer Advances in Neural Information Processing Systems 34, 14488-14501, 2021	40	2021
Summarization of Multi-Document Topic Hierarchies using Submodular Mixtures RB Bairi, R Iyer, G Ramakrishnan, J Bilmes In Association of Computational Linguists (ACL) 2015, 2015	40	2015
Prism: A rich class of parameterized submodular information measures for guided data subset selection S Kothawade, V Kaushal, G Ramakrishnan, J Bilmes, R Iyer Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 10238 …, 2022	38*	2022
Active machine learning DM Chickering, CA Meek, PY Simard, RK Iyer US Patent 10,262,272, 2019	38	2019
Gcr: Gradient coreset based replay buffer selection for continual learning R Tiwari, K Killamsetty, R Iyer, P Shenoy Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022	35	2022

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