Submodularity in data subset selection and active learning K Wei, R Iyer, J Bilmes International conference on machine learning, 1954-1963, 2015 | 455 | 2015 |
Submodular optimization with submodular cover and submodular knapsack constraints RK Iyer, JA Bilmes Advances in Neural Information Processing Systems (NIPS), 2436-2444, 2013 | 305 | 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 | 238 | 2014 |
Glister: A generalization based data selection framework for efficient and robust learning K Killamsetty, D Subramanian, G Ramakrishnan, R Iyer Proceedings of the AAAI Conference on Artificial Intelligence, 2021 | 192* | 2021 |
Algorithms for approximate minimization of the difference between submodular functions, with applications R Iyer, J Bilmes Uncertainty in Artificial Intelligence (UAI), 2012 | 186 | 2012 |
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 | 181 | 2021 |
Fast semidifferential-based submodular function optimization R Iyer, S Jegelka, J Bilmes International Conference on Machine Learning (ICML), 2013 | 145 | 2013 |
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 | 119 | 2013 |
Fast multi-stage submodular maximization K Wei, R Iyer, J Bilmes International Conference on Machine Learning (ICML-14), 1494-1502, 2014 | 108 | 2014 |
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 | 100 | 2022 |
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 | 91 | 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 | 89 | 2021 |
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 | 75 | 2021 |
Submodular combinatorial information measures with applications in machine learning R Iyer, N Khargoankar, J Bilmes, H Asanani Algorithmic Learning Theory, 722-754, 2021 | 75 | 2021 |
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 | 61* | 2022 |
Submodular-Bregman and the Lovasz-Bregman Divergences with Applications R Iyer, J Bilmes Advances in Neural Information Processing Systems (NIPS), 2942-2950, 2012 | 56 | 2012 |
Active machine learning DM Chickering, CA Meek, PY Simard, RK Iyer US Patent 10,262,272, 2019 | 49 | 2019 |
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 | 47 | 2015 |
Algorithms for optimizing the ratio of submodular functions W Bai, R Iyer, K Wei, J Bilmes International Conference on Machine Learning, 2751-2759, 2016 | 46 | 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 | 44 | 2015 |