Seuraa
Sanghamitra Dutta
Sanghamitra Dutta
Faculty, University of Maryland College Park
Vahvistettu sähköpostiosoite verkkotunnuksessa umd.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
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
3952016
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
2512019
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
International conference on artificial intelligence and statistics, 803-812, 2018
1672018
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
1412017
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
1242020
A unified coded deep neural network training strategy based on generalized polydot codes
S Dutta, Z Bai, H Jeong, TM Low, P Grover
2018 IEEE International Symposium on Information Theory (ISIT), 1585-1589, 2018
1072018
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
832017
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
432018
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
262021
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
262020
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
262019
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
232021
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
202022
Information flow in computational systems
P Venkatesh, S Dutta, P Grover
IEEE Transactions on Information Theory 66 (9), 5456-5491, 2020
202020
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
152020
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
92021
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
92019
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
82021
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
82020
Monopoly: Financial prediction from monetary policy conference videos using multimodal cues
P Mathur, A Neerkaje, M Chhibber, R Sawhney, F Guo, F Dernoncourt, ...
Proceedings of the 30th ACM International Conference on Multimedia, 2276-2285, 2022
42022
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