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Rishabh Iyer
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Year
Submodularity in data subset selection and active learning
K Wei, R Iyer, J Bilmes
International conference on machine learning, 1954-1963, 2015
3432015
Submodular optimization with submodular cover and submodular knapsack constraints
RK Iyer, JA Bilmes
Advances in Neural Information Processing Systems (NIPS), 2436-2444, 2013
2592013
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
2082014
Algorithms for approximate minimization of the difference between submodular functions, with applications
R Iyer, J Bilmes
Uncertainty in Artificial Intelligence (UAI), 2012
1642012
Fast semidifferential-based submodular function optimization
R Iyer, S Jegelka, J Bilmes
International Conference on Machine Learning (ICML), 2013
1332013
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
1112013
Fast multi-stage submodular maximization
K Wei, R Iyer, J Bilmes
International Conference on Machine Learning (ICML-14), 1494-1502, 2014
952014
Glister: A generalization based data selection framework for efficient and robust learning
K Killamsetty, D Subramanian, G Ramakrishnan, R Iyer
AAAI, 2021
91*2021
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
832021
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
66*2019
Submodular-Bregman and the Lovasz-Bregman Divergences with Applications
R Iyer, J Bilmes
Advances in Neural Information Processing Systems (NIPS), 2942-2950, 2012
512012
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
452021
Submodular combinatorial information measures with applications in machine learning
R Iyer, N Khargoankar, J Bilmes, H Asanani
Algorithmic Learning Theory, 722-754, 2021
452021
Algorithms for optimizing the ratio of submodular functions
W Bai, R Iyer, K Wei, J Bilmes
International Conference on Machine Learning, 2751-2759, 2016
412016
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
402015
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
392015
Active machine learning
DM Chickering, CA Meek, PY Simard, RK Iyer
US Patent 10,262,272, 2019
352019
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
33*2022
Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications
R Iyer
Ph.D Dissertation, 2015
332015
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
312021
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