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ORCIDSanghamitra Dutta
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2020 – today
- 2024
[j10]Faisal Hamman
, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta:
Robust Algorithmic Recourse Under Model Multiplicity With Probabilistic Guarantees. IEEE J. Sel. Areas Inf. Theory 5: 357-368 (2024)- [j9]Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. Trans. Mach. Learn. Res. 2024 (2024) - [c21]Faisal Hamman, Sanghamitra Dutta:
Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. ICLR 2024 - [c20]Faisal Hamman, Sanghamitra Dutta:
A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition. ISIT 2024: 214-219 - [i29]Shubham Sharma, Sanghamitra Dutta, Emanuele Albini, Freddy Lécué, Daniele Magazzeni, Manuela Veloso:
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values. CoRR abs/2403.08880 (2024) - [i28]Pasan Dissanayake, Sanghamitra Dutta:
Model Reconstruction Using Counterfactual Explanations: Mitigating the Decision Boundary Shift. CoRR abs/2405.05369 (2024) - [i27]Faisal Hamman, Sanghamitra Dutta:
A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition. CoRR abs/2406.04562 (2024) - [i26]Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta:
Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition. CoRR abs/2407.00482 (2024) - [i25]Faisal Hamman, Pasan Dissanayake, Saumitra Mishra, Freddy Lécué, Sanghamitra Dutta:
Quantifying Prediction Consistency Under Model Multiplicity in Tabular LLMs. CoRR abs/2407.04173 (2024) - [i24]Mohamed Nomeir, Pasan Dissanayake, Shreya Meel, Sanghamitra Dutta, Sennur Ulukus:
Private Counterfactual Retrieval. CoRR abs/2410.13812 (2024) - 2023
- [j8]Sanghamitra Dutta, Faisal Hamman:
A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability. Entropy 25(5): 795 (2023) - [j7]Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. Trans. Mach. Learn. Res. 2023 (2023) - [c19]Shubham Sharma, Sanghamitra Dutta, Emanuele Albini, Freddy Lécué, Daniele Magazzeni, Manuela Veloso:
REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values. AIES 2023: 443-453 - [c18]Faisal Hamman, Jiahao Chen, Sanghamitra Dutta:
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity. FAccT 2023: 1358-1368 - [c17]Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta:
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. ICML 2023: 12351-12367 - [c16]Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka:
In- or out-of-distribution detection via dual divergence estimation. UAI 2023: 635-646 - [i23]Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. CoRR abs/2302.01385 (2023) - [i22]Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta:
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. CoRR abs/2305.11997 (2023) - [i21]Faisal Hamman, Sanghamitra Dutta:
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. CoRR abs/2307.11333 (2023) - 2022
- [b1]Sanghamitra Dutta:
Strategies for Fair, Explainable, and Reliable Machine Learning Using Information Theory. Carnegie Mellon University, USA, 2022 - [c15]Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni:
Robust Counterfactual Explanations for Tree-Based Ensembles. ICML 2022: 5742-5756 - [c14]Puneet Mathur, Atula Tejaswi Neerkaje, Malika Chhibber, Ramit Sawhney, Fuming Guo, Franck Dernoncourt, Sanghamitra Dutta, Dinesh Manocha:
MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues. ACM Multimedia 2022: 2276-2285 - [i20]Sanghamitra Dutta, Praveen Venkatesh, Pulkit Grover:
Quantifying Feature Contributions to Overall Disparity Using Information Theory. CoRR abs/2206.08454 (2022) - [i19]Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale. CoRR abs/2206.14853 (2022) - [i18]Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni:
Robust Counterfactual Explanations for Tree-Based Ensembles. CoRR abs/2207.02739 (2022) - [i17]Faisal Hamman, Jiahao Chen, Sanghamitra Dutta:
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity. CoRR abs/2211.02139 (2022) - 2021
- [j6]Sanghamitra Dutta, Jianyu Wang, Gauri Joshi:
Slow and Stale Gradients Can Win the Race. IEEE J. Sel. Areas Inf. Theory 2(3): 1012-1024 (2021) - [j5]Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover:
Fairness Under Feature Exemptions: Counterfactual and Observational Measures. IEEE Trans. Inf. Theory 67(10): 6675-6710 (2021) - [c13]Chenyu Jiang, Bowen Wu, Sanghamitra Dutta, Pulkit Grover:
An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles. BIAS 2021: 117-129 - [c12]Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover:
Can Information Flows Suggest Targets for Interventions in Neural Circuits? NeurIPS 2021: 3149-3162 - [i16]Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel R. Tetreault, Alex Jaimes:
GTN-ED: Event Detection Using Graph Transformer Networks. CoRR abs/2104.15104 (2021) - [i15]Saumitra Mishra, Sanghamitra Dutta, Jason Long, Daniele Magazzeni:
A Survey on the Robustness of Feature Importance and Counterfactual Explanations. CoRR abs/2111.00358 (2021) - [i14]Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover:
Can Information Flows Suggest Targets for Interventions in Neural Circuits? CoRR abs/2111.05299 (2021) - 2020
- [j4]Sanghamitra Dutta, Haewon Jeong, Yaoqing Yang, Viveck R. Cadambe, Tze Meng Low, Pulkit Grover:
Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing. Proc. IEEE 108(8): 1219-1234 (2020) - [j3]Sanghamitra Dutta, Mohammad Fahim, Farzin Haddadpour, Haewon Jeong, Viveck R. Cadambe, Pulkit Grover:
On the Optimal Recovery Threshold of Coded Matrix Multiplication. IEEE Trans. Inf. Theory 66(1): 278-301 (2020) - [j2]Praveen Venkatesh, Sanghamitra Dutta, Pulkit Grover:
Information Flow in Computational Systems. IEEE Trans. Inf. Theory 66(9): 5456-5491 (2020) - [c11]Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover:
An Information-Theoretic Quantification of Discrimination with Exempt Features. AAAI 2020: 3825-3833 - [c10]Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney:
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. ICML 2020: 2803-2813 - [c9]Praveen Venkatesh, Sanghamitra Dutta, Pulkit Grover:
How else can we define Information Flow in Neural Circuits? ISIT 2020: 2879-2884 - [i13]Sanghamitra Dutta, Jianyu Wang, Gauri Joshi:
Slow and Stale Gradients Can Win the Race. CoRR abs/2003.10579 (2020) - [i12]Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover:
Fairness Under Feature Exemptions: Counterfactual and Observational Measures. CoRR abs/2006.07986 (2020)
2010 – 2019
- 2019
- [j1]Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
"Short-Dot": Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. IEEE Trans. Inf. Theory 65(10): 6171-6193 (2019) - [c8]Praveen Venkatesh, Sanghamitra Dutta, Pulkit Grover:
How should we define Information Flow in Neural Circuits? ISIT 2019: 176-180 - [i11]Praveen Venkatesh, Sanghamitra Dutta, Pulkit Grover:
Information Flow in Computational Systems. CoRR abs/1902.02292 (2019) - [i10]Sanghamitra Dutta, Ziqian Bai, Tze Meng Low, Pulkit Grover:
CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors. CoRR abs/1903.01042 (2019) - [i9]Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney:
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy. CoRR abs/1910.07870 (2019) - 2018
- [c7]Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar:
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. AISTATS 2018: 803-812 - [c6]Utsav Sheth, Sanghamitra Dutta, Malhar Chaudhari, Haewon Jeong, Yaoqing Yang, Jukka Kohonen, Teemu Roos, Pulkit Grover:
An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation. IEEE BigData 2018: 1113-1120 - [c5]Sanghamitra Dutta, Ziqian Bai, Haewon Jeong, Tze Meng Low, Pulkit Grover:
A Unified Coded Deep Neural Network Training Strategy based on Generalized PolyDot codes. ISIT 2018: 1585-1589 - [i8]Sanghamitra Dutta, Mohammad Fahim, Farzin Haddadpour, Haewon Jeong, Viveck R. Cadambe, Pulkit Grover:
On the Optimal Recovery Threshold of Coded Matrix Multiplication. CoRR abs/1801.10292 (2018) - [i7]Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar:
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. CoRR abs/1803.01113 (2018) - [i6]Sanghamitra Dutta, Ziqian Bai, Haewon Jeong, Tze Meng Low, Pulkit Grover:
A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication. CoRR abs/1811.10751 (2018) - [i5]Utsav Sheth, Sanghamitra Dutta, Malhar Chaudhari, Haewon Jeong, Yaoqing Yang, Jukka Kohonen, Teemu Roos, Pulkit Grover:
An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation. CoRR abs/1811.11811 (2018) - 2017
- [c4]Mohammad Fahim, Haewon Jeong, Farzin Haddadpour, Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
On the optimal recovery threshold of coded matrix multiplication. Allerton 2017: 1264-1270 - [c3]Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
Coded convolution for parallel and distributed computing within a deadline. ISIT 2017: 2403-2407 - [i4]Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
"Short-Dot": Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. CoRR abs/1704.05181 (2017) - [i3]Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
Coded convolution for parallel and distributed computing within a deadline. CoRR abs/1705.03875 (2017) - 2016
- [c2]Sanghamitra Dutta, Pulkit Grover:
Adaptivity provably helps: Information-theoretic limits on l0 cost of non-adaptive sensing. ISIT 2016: 1431-1435 - [c1]Sanghamitra Dutta, Viveck R. Cadambe, Pulkit Grover:
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. NIPS 2016: 2092-2100 - [i2]Sanghamitra Dutta, Pulkit Grover:
Adaptivity provably helps: information-theoretic limits on $l_0$ cost of non-adaptive sensing. CoRR abs/1602.04260 (2016) - 2014
- [i1]Sanghamitra Dutta, Arijit De:
LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging. CoRR abs/1411.4020 (2014)
Coauthor Index
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