フォロー
Rishabh Iyer
タイトル
引用先
引用先
Submodularity in data subset selection and active learning
K Wei, R Iyer, J Bilmes
International conference on machine learning, 1954-1963, 2015
2812015
Submodular optimization with submodular cover and submodular knapsack constraints
RK Iyer, JA Bilmes
Advances in Neural Information Processing Systems (NIPS), 2436-2444, 2013
2262013
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
1942014
Algorithms for approximate minimization of the difference between submodular functions, with applications
R Iyer, J Bilmes
Uncertainty in Artificial Intelligence (UAI), 2012
1412012
Fast semidifferential-based submodular function optimization
R Iyer, S Jegelka, J Bilmes
International Conference on Machine Learning (ICML), 2013
1232013
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
1022013
Fast multi-stage submodular maximization
K Wei, R Iyer, J Bilmes
International Conference on Machine Learning (ICML-14), 1494-1502, 2014
822014
Submodular-Bregman and the Lovasz-Bregman Divergences with Applications
R Iyer, J Bilmes
Advances in Neural Information Processing Systems (NIPS), 2942-2950, 2012
502012
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
43*2019
Glister: A generalization based data selection framework for efficient and robust learning
K Killamsetty, D Subramanian, G Ramakrishnan, R Iyer
AAAI, 2021
42*2021
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
342015
Monotone closure of relaxed constraints in submodular optimization: Connections between minimization and maximization: Extended version
R Iyer, S Jegelka, J Bilmes
UAI, 2014
312014
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
302021
Algorithms for optimizing the ratio of submodular functions
W Bai, R Iyer, K Wei, J Bilmes
International Conference on Machine Learning, 2751-2759, 2016
302016
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
292015
Submodular Point Processes
R Iyer, J Bilmes
Proc. Artificial Intelligence and Statistics (AISTATS), 2015
28*2015
Submodular combinatorial information measures with applications in machine learning
R Iyer, N Khargoankar, J Bilmes, H Asanani
Algorithmic Learning Theory, 722-754, 2021
272021
Submodular Optimization and Machine Learning: Theoretical Results, Unifying and Scalable Algorithms, and Applications
R Iyer
Ph.D Dissertation, 2015
272015
Polyhedral aspects of submodularity, convexity and concavity
R Iyer, J Bilmes
arXiv preprint arXiv:1506.07329, 2015
262015
Active machine learning
DM Chickering, CA Meek, PY Simard, RK Iyer
US Patent 10,262,272, 2019
242019
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