LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging. - Sanghamitra Dutta; Arijit De (CoRR, 2014)

Adaptivity provably helps: Information-theoretic limits on l0 cost of non-adaptive sensing. - Sanghamitra Dutta; Pulkit Grover (ISIT, 2016)

Adaptivity provably helps: information-theoretic limits on $l_0$ cost of non-adaptive sensing. - Sanghamitra Dutta; Pulkit Grover (CoRR, 2016)

Coded convolution for parallel and distributed computing within a deadline. - Sanghamitra Dutta; Viveck R. Cadambe; Pulkit Grover (ISIT, 2017)

Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD. - Sanghamitra Dutta; Gauri Joshi; Soumyadip Ghosh; Parijat Dube; Priya Nagpurkar (AISTATS, 2018)

An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation. - Utsav Sheth; Sanghamitra Dutta; Malhar Chaudhari; Haewon Jeong; Yaoqing Yang; Jukka Kohonen; Teemu Roos; Pulkit Grover (IEEE BigData, 2018)

A Unified Coded Deep Neural Network Training Strategy based on Generalized PolyDot codes. - Sanghamitra Dutta; Ziqian Bai; Haewon Jeong; Tze Meng Low; Pulkit Grover (ISIT, 2018)

A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication. - Sanghamitra Dutta; Ziqian Bai; Haewon Jeong; Tze Meng Low; Pulkit Grover (CoRR, 2018)

How should we define Information Flow in Neural Circuits? - Praveen Venkatesh; Sanghamitra Dutta; Pulkit Grover (ISIT, 2019)

CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors. - Sanghamitra Dutta; Ziqian Bai; Tze Meng Low; Pulkit Grover (CoRR, 2019)

An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy. - Sanghamitra Dutta; Dennis Wei; Hazar Yueksel; Pin-Yu Chen; Sijia Liu 001; Kush R. Varshney (CoRR, 2019)

"Short-Dot": Computing Large Linear Transforms Distributedly Using Coded Short Dot Products. - Sanghamitra Dutta; Viveck R. Cadambe; Pulkit Grover (IEEE Trans. Inf. Theory, 2019)

An Information-Theoretic Quantification of Discrimination with Exempt Features. - Sanghamitra Dutta; Praveen Venkatesh; Piotr Mardziel; Anupam Datta; Pulkit Grover (AAAI, 2020)

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing. - Sanghamitra Dutta; Dennis Wei; Hazar Yueksel; Pin-Yu Chen; Sijia Liu 001; Kush R. Varshney (ICML, 2020)

How else can we define Information Flow in Neural Circuits? - Praveen Venkatesh; Sanghamitra Dutta; Pulkit Grover (ISIT, 2020)

Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing. - Sanghamitra Dutta; Haewon Jeong; Yaoqing Yang; Viveck R. Cadambe; Tze Meng Low; Pulkit Grover (Proc. IEEE, 2020)

On the Optimal Recovery Threshold of Coded Matrix Multiplication. - Sanghamitra Dutta; Mohammad Fahim; Farzin Haddadpour; Haewon Jeong; Viveck R. Cadambe; Pulkit Grover (IEEE Trans. Inf. Theory, 2020)

Information Flow in Computational Systems. - Praveen Venkatesh; Sanghamitra Dutta; Pulkit Grover (IEEE Trans. Inf. Theory, 2020)

An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles. - Chenyu Jiang; Bowen Wu; Sanghamitra Dutta; Pulkit Grover (BIAS, 2021)

Can Information Flows Suggest Targets for Interventions in Neural Circuits? - Praveen Venkatesh; Sanghamitra Dutta; Neil Mehta; Pulkit Grover (NeurIPS, 2021)

GTN-ED: Event Detection Using Graph Transformer Networks. - Sanghamitra Dutta; Liang Ma; Tanay Kumar Saha; Di Lu 003; Joel R. Tetreault; Alex Jaimes (CoRR, 2021)

A Survey on the Robustness of Feature Importance and Counterfactual Explanations. - Saumitra Mishra; Sanghamitra Dutta; Jason Long; Daniele Magazzeni (CoRR, 2021)

Slow and Stale Gradients Can Win the Race. - Sanghamitra Dutta; Jianyu Wang; Gauri Joshi (IEEE J. Sel. Areas Inf. Theory, 2021)

Fairness Under Feature Exemptions: Counterfactual and Observational Measures. - Sanghamitra Dutta; Praveen Venkatesh; Piotr Mardziel; Anupam Datta; Pulkit Grover (IEEE Trans. Inf. Theory, 2021)

Robust Counterfactual Explanations for Tree-Based Ensembles. - Sanghamitra Dutta; Jason Long; Saumitra Mishra; Cecilia Tilli; Daniele Magazzeni (ICML, 2022)

MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues. - Puneet Mathur; Atula Tejaswi Neerkaje; Malika Chhibber; Ramit Sawhney; Fuming Guo; Franck Dernoncourt; Sanghamitra Dutta; Dinesh Manocha (ACM Multimedia, 2022)

Quantifying Feature Contributions to Overall Disparity Using Information Theory. - Sanghamitra Dutta; Praveen Venkatesh; Pulkit Grover (CoRR, 2022)

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale. - Akshaj Kumar Veldanda; Ivan Brugere; Jiahao Chen; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (CoRR, 2022)

Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity. - Faisal Hamman; Jiahao Chen; Sanghamitra Dutta (CoRR, 2022)

Strategies for Fair, Explainable, and Reliable Machine Learning Using Information Theory. - Sanghamitra Dutta (2022)

REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values. - Shubham Sharma; Sanghamitra Dutta; Emanuele Albini; Freddy Lécué; Daniele Magazzeni; Manuela Veloso (AIES, 2023)

Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity. - Faisal Hamman; Jiahao Chen 001; Sanghamitra Dutta (FAccT, 2023)

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. - Faisal Hamman; Erfaun Noorani; Saumitra Mishra; Daniele Magazzeni; Sanghamitra Dutta (ICML, 2023)

In- or out-of-distribution detection via dual divergence estimation. - Sahil Garg; Sanghamitra Dutta; Mina Dalirrooyfard; Anderson Schneider; Yuriy Nevmyvaka (UAI, 2023)

Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. - Faisal Hamman; Sanghamitra Dutta (CoRR, 2023)

A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability. - Sanghamitra Dutta; Faisal Hamman (Entropy, 2023)

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss. - Akshaj Kumar Veldanda; Ivan Brugere; Jiahao Chen; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (Trans. Mach. Learn. Res., 2023)

Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. - Faisal Hamman; Sanghamitra Dutta (ICLR, 2024)

A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition. - Faisal Hamman; Sanghamitra Dutta (ISIT, 2024)

Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory. - Pasan Dissanayake; Sanghamitra Dutta (NeurIPS, 2024)

Model Reconstruction Using Counterfactual Explanations: Mitigating the Decision Boundary Shift. - Pasan Dissanayake; Sanghamitra Dutta (CoRR, 2024)

Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition. - Barproda Halder; Faisal Hamman; Pasan Dissanayake; Qiuyi Zhang 001; Ilia Sucholutsky; Sanghamitra Dutta (CoRR, 2024)

Quantifying Prediction Consistency Under Model Multiplicity in Tabular LLMs. - Faisal Hamman; Pasan Dissanayake; Saumitra Mishra; Freddy Lécué; Sanghamitra Dutta (CoRR, 2024)

Private Counterfactual Retrieval. - Mohamed W. Nomeir; Pasan Dissanayake; Shreya Meel; Sanghamitra Dutta; Sennur Ulukus (CoRR, 2024)

Private Counterfactual Retrieval With Immutable Features. - Shreya Meel; Pasan Dissanayake; Mohamed W. Nomeir; Sanghamitra Dutta; Sennur Ulukus (CoRR, 2024)

Robust Algorithmic Recourse Under Model Multiplicity With Probabilistic Guarantees. - Faisal Hamman; Erfaun Noorani; Saumitra Mishra; Daniele Magazzeni; Sanghamitra Dutta (IEEE J. Sel. Areas Inf. Theory, 2024)

Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. - Akshaj Kumar Veldanda; Ivan Brugere; Sanghamitra Dutta; Alan Mishler; Siddharth Garg (Trans. Mach. Learn. Res., 2024)

Quantifying Knowledge Distillation using Partial Information Decomposition. - Pasan Dissanayake; Faisal Hamman; Barproda Halder; Ilia Sucholutsky; Qiuyi Zhang 001; Sanghamitra Dutta (AISTATS, 2025)

Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures. - Erfaun Noorani; Pasan Dissanayake; Faisal Hamman; Sanghamitra Dutta (AAMAS, 2025)

Demystifying the Accuracy-Interpretability Trade-Off: A Case Study of Inferring Ratings from Reviews. - Pranjal Atrey; Michael P. Brundage; Min Wu; Sanghamitra Dutta (CoRR, 2025)

T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning. - Yanjun Fu; Faisal Hamman; Sanghamitra Dutta (CoRR, 2025)

What If, But Privately: Private Counterfactual Retrieval. - Shreya Meel; Mohamed W. Nomeir; Pasan Dissanayake; Sanghamitra Dutta; Sennur Ulukus (CoRR, 2025)