| Short-dot: Computing large linear transforms distributedly using coded short dot products S Dutta, V Cadambe, P Grover Advances In Neural Information Processing Systems 29, 2016 | 373 | 2016 |
| On the optimal recovery threshold of coded matrix multiplication S Dutta, M Fahim, F Haddadpour, H Jeong, V Cadambe, P Grover IEEE Transactions on Information Theory 66 (1), 278-301, 2019 | 223 | 2019 |
| On the optimal recovery threshold of coded matrix multiplication S Dutta, M Fahim, F Haddadpour, H Jeong, V Cadambe, P Grover IEEE Transactions on Information Theory, 2019 | 223 | 2019 |
| Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD S Dutta, G Joshi, S Ghosh, P Dube, P Nagpurkar arXiv preprint arXiv:1803.01113, 2018 | 168 | 2018 |
| Coded convolution for parallel and distributed computing within a deadline S Dutta, V Cadambe, P Grover 2017 IEEE International Symposium on Information Theory (ISIT), 2403-2407, 2017 | 133 | 2017 |
| Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing S Dutta, D Wei, H Yueksel, PY Chen, S Liu, KR Varshney International Conference on Machine Learning, 2020 | 103* | 2020 |
| A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication S Dutta, Z Bai, H Jeong, TM Low, P Grover arXiv preprint arXiv:1811.10751, 2018 | 100 | 2018 |
| On the optimal recovery threshold of coded matrix multiplication M Fahim, H Jeong, F Haddadpour, S Dutta, V Cadambe, P Grover 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 80 | 2017 |
| An application of storage-optimal matdot codes for coded matrix multiplication: Fast k-nearest neighbors estimation U Sheth, S Dutta, M Chaudhari, H Jeong, Y Yang, J Kohonen, T Roos, ... 2018 IEEE International Conference on Big Data (Big Data), 1113-1120, 2018 | 43 | 2018 |
| CodeNet: Training large scale neural networks in presence of soft-errors S Dutta, Z Bai, TM Low, P Grover arXiv preprint arXiv:1903.01042, 2019 | 23 | 2019 |
| An information-theoretic quantification of discrimination with exempt features S Dutta, P Venkatesh, P Mardziel, A Datta, P Grover Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3825-3833, 2020 | 22 | 2020 |
| Information flow in computational systems P Venkatesh, S Dutta, P Grover IEEE Transactions on Information Theory 66 (9), 5456-5491, 2020 | 19 | 2020 |
| A survey on the robustness of feature importance and counterfactual explanations S Mishra, S Dutta, J Long, D Magazzeni arXiv preprint arXiv:2111.00358, 2021 | 15 | 2021 |
| Slow and stale gradients can win the race S Dutta, J Wang, G Joshi IEEE Journal on Selected Areas in Information Theory 2 (3), 1012-1024, 2021 | 14 | 2021 |
| Addressing unreliability in emerging devices and Non-Von Neumann architectures using coded computing S Dutta, H Jeong, Y Yang, V Cadambe, TM Low, P Grover Proceedings of the IEEE 108 (8), 1219-1234, 2020 | 13 | 2020 |
| Robust counterfactual explanations for tree-based ensembles S Dutta, J Long, S Mishra, C Tilli, D Magazzeni International Conference on Machine Learning, 5742-5756, 2022 | 12 | 2022 |
| How should we define information flow in neural circuits? P Venkatesh, S Dutta, P Grover 2019 IEEE international symposium on information theory (ISIT), 176-180, 2019 | 9 | 2019 |
| Gtn-ed: Event detection using graph transformer networks S Dutta, L Ma, TK Saha, D Lu, J Tetreault, A Jaimes arXiv preprint arXiv:2104.15104, 2021 | 8 | 2021 |
| How else can we define information flow in neural circuits? P Venkatesh, S Dutta, P Grover 2020 IEEE International Symposium on Information Theory (ISIT), 2879-2884, 2020 | 8 | 2020 |
| Fairness under feature exemptions: Counterfactual and observational measures S Dutta, P Venkatesh, P Mardziel, A Datta, P Grover IEEE Transactions on Information Theory 67 (10), 6675-6710, 2021 | 6 | 2021 |
Pulkit GroverAngel Jordan Professor of Electrical and Computer Engineering, Biomedical Engineering, Neuroscience在 cmu.edu 的电子邮件经过验证
