| Co-regularized multi-view spectral clustering A Kumar, P Rai, H Daume Advances in neural information processing systems (NIPS), 1413-1421, 2011 | 764 | 2011 |
| Generalized multiview analysis: A discriminative latent space A Sharma, A Kumar, H Daume, DW Jacobs 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2160-2167, 2012 | 621 | 2012 |
| A co-training approach for multi-view spectral clustering A Kumar, H Daumé Proceedings of the 28th international conference on machine learning (ICML …, 2011 | 611 | 2011 |
| Learning task grouping and overlap in multi-task learning A Kumar, H Daume III Proceedings of the 29th International Coference on Machine Learning (ICML), 2012 | 424 | 2012 |
| Frustratingly easy semi-supervised domain adaptation H Daumé III, A Kumar, A Saha Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language …, 2010 | 207 | 2010 |
| Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification Y Lu, A Kumar, S Zhai, Y Cheng, T Javidi, R Feris Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 204 | 2017 |
| BlockDrop: Dynamic Inference Paths in Residual Networks Z Wu, T Nagarajan, A Kumar, S Rennie, LS Davis, K Grauman, R Feris Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 178 | 2018 |
| Co-regularization based semi-supervised domain adaptation A Kumar, A Saha, H Daume Advances in neural information processing systems (NIPS), 478-486, 2010 | 167 | 2010 |
| Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization A Kumar, V Sindhwani, P Kambadur Proceedings of the 30th International Conference on Machine Learning (ICML), 2013 | 160 | 2013 |
| Variational Inference of Disentangled Latent Concepts from Unlabeled Observations A Kumar, P Sattigeri, A Balakrishnan International Conference on Learning Representations (ICLR), 2018 | 158 | 2018 |
| Delta-encoder: an effective sample synthesis method for few-shot object recognition E Schwartz, L Karlinsky, J Shtok, S Harary, M Marder, A Kumar, R Feris, ... Advances in Neural Information Processing Systems, 2845-2855, 2018 | 96 | 2018 |
| Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference A Kumar, P Sattigeri, T Fletcher Advances in Neural Information Processing Systems (NIPS), 5540-5550, 2017 | 88 | 2017 |
| Spottune: transfer learning through adaptive fine-tuning Y Guo, H Shi, A Kumar, K Grauman, T Rosing, R Feris Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 70 | 2019 |
| Handbook of robust low-rank and sparse matrix decomposition: Applications in image and video processing T Bouwmans, NS Aybat, E Zahzah CRC Press, 2016 | 69 | 2016 |
| Co-regularized alignment for unsupervised domain adaptation A Kumar, P Sattigeri, K Wadhawan, L Karlinsky, R Feris, B Freeman, ... Advances in Neural Information Processing Systems 31, 9345-9356, 2018 | 62 | 2018 |
| Simultaneously leveraging output and task structures for multiple-output regression P Rai, A Kumar, H Daume Advances in Neural Information Processing Systems (NIPS), 3185-3193, 2012 | 59 | 2012 |
| A Binary Classification Framework for Two-Stage Multiple Kernel Learning A Kumar, A Niculescu-Mizil, K Kavukcoglu, H Daumé III Proceedings of the 29th International Conference on Machine Learning (ICML), 2012 | 57 | 2012 |
| The riemannian geometry of deep generative models H Shao, A Kumar, P Thomas Fletcher Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 56 | 2018 |
| S3pool: Pooling with stochastic spatial sampling S Zhai, H Wu, A Kumar, Y Cheng, Y Lu, Z Zhang, R Feris Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 35 | 2017 |
| Improved semi-supervised learning with gans using manifold invariances A Kumar, P Sattigeri, PT Fletcher arXiv preprint arXiv:1705.08850, 2017 | 32 | 2017 |
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
