| Gradient descent provably optimizes over-parameterized neural networks SS Du, X Zhai, B Poczos, A Singh arXiv preprint arXiv:1810.02054, 2018 | 867 | 2018 |
| Data Poisoning Attacks on Factorization-Based Collaborative Filtering B Li, Y Wang, A Singh, Y Vorobeychik Advances in Neural Information Processing Systems, 1885-1893, 2016 | 431 | 2016 |
| Confidence sets for persistence diagrams BT Fasy, F Lecci, A Rinaldo, L Wasserman, S Balakrishnan, A Singh The Annals of Statistics 42 (6), 2301-2339, 2014 | 369 | 2014 |
| Unlabeled data: Now it helps, now it doesn't A Singh, R Nowak, X Zhu Advances in Neural Information Processing Systems, 1513-1520, 2009 | 355 | 2009 |
| Gradient descent can take exponential time to escape saddle points SS Du, C Jin, JD Lee, MI Jordan, A Singh, B Poczos Advances in neural information processing systems, 1067-1077, 2017 | 318 | 2017 |
| Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima SS Du, JD Lee, Y Tian, B Poczos, A Singh arXiv preprint arXiv:1712.00779, 2017 | 256 | 2017 |
| On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions A Ramdas, SJ Reddi, B Póczos, A Singh, L Wasserman Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015 | 207 | 2015 |
| Multi-manifold semi-supervised learning AB Goldberg, X Zhu, A Singh, Z Xu, R Nowak International Conference on Artificial Intelligence and Statistics, 169-176, 2009 | 207 | 2009 |
| Decentralized compression and predistribution via randomized gossiping M Rabbat, J Haupt, A Singh, R Nowak Proceedings of the 5th international conference on Information processing in …, 2006 | 207 | 2006 |
| Low-Rank Matrix and Tensor Completion via Adaptive Sampling A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 836-844, 2013 | 195 | 2013 |
| Quantifying Differences and Similarities in Whole-brain White Matter Architecture Using Local Connectome Fingerprints FC Yeh, JM Vettel, A Singh, B Poczos, ST Grafton, KI Erickson, WYI Tseng, ... PLOS Computational Biology 12 (11), e1005203, 2016 | 161 | 2016 |
| Computationally Efficient Robust Sparse Estimation in High Dimensions S Balakrishnan, SS Du, J Li, A Singh Conference on Learning Theory, 169-212, 2017 | 147 | 2017 |
| Signal recovery on graphs: Fundamental limits of sampling strategies S Chen, R Varma, A Singh, J Kovačević IEEE Transactions on Signal and Information Processing over Networks 2 (4 …, 2016 | 145 | 2016 |
| Stochastic Zeroth-order Optimization in High Dimensions Y Wang, S Du, S Balakrishnan, A Singh arXiv preprint arXiv:1710.10551, 2017 | 138 | 2017 |
| Near-Optimal Design of Experiments via Regret Minimization Z Allen-Zhu, Y Li, A Singh, Y Wang International Conference on Machine Learning, 126-135, 2017 | 135* | 2017 |
| Active learning for adaptive mobile sensing networks A Singh, R Nowak, P Ramanathan Proceedings of the 5th international conference on Information processing in …, 2006 | 132 | 2006 |
| Noise thresholds for spectral clustering S Balakrishnan, M Xu, A Krishnamurthy, A Singh Advances in Neural Information Processing Systems, 954-962, 2011 | 120 | 2011 |
| Distribution-Free Distribution Regression. B Póczos, A Singh, A Rinaldo, LA Wasserman AISTATS, 507-515, 2013 | 119 | 2013 |
| Efficient Active Algorithms for Hierarchical Clustering A Krishnamurthy, S Balakrishnan, M Xu, A Singh Arxiv preprint arXiv:1206.4672, 2012 | 103 | 2012 |
| Adaptive hausdorff estimation of density level sets A Singh, C Scott, R Nowak The Annals of Statistics 37 (5B), 2760-2782, 2009 | 97 | 2009 |
Larry WassermanProfessor, Department of Statistics and Machine Learning Department, Carnegie Mellon UniversityVahvistettu sähköpostiosoite verkkotunnuksessa stat.cmu.edu
