Dutta, Sanghamitra

20 publications

NeurIPS 2025 Few-Shot Knowledge Distillation of LLMs with Counterfactual Explanations Faisal Hamman, Pasan Dissanayake, Yanjun Fu, Sanghamitra Dutta

AISTATS 2025 Quantifying Knowledge Distillation Using Partial Information Decomposition Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta

ICML 2025 Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs Faisal Hamman, Pasan Dissanayake, Saumitra Mishra, Freddy Lecue, Sanghamitra Dutta

NeurIPS 2025 T-SHIRT: Token-Selective Hierarchical Data Selection for Instruction Tuning Yanjun Fu, Faisal Hamman, Sanghamitra Dutta

TMLR 2025 Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta

ICLR 2024 Demystifying Local & Global Fairness Trade-Offs in Federated Learning Using Partial Information Decomposition Faisal Hamman, Sanghamitra Dutta

NeurIPSW 2024 Formalizing Limits of Knowledge Distillation Using Partial Information Decomposition Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Qiuyi Zhang, Sanghamitra Dutta

TMLR 2024 Hyper-Parameter Tuning for Fair Classification Without Sensitive Attribute Access Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg

NeurIPS 2024 Model Reconstruction Using Counterfactual Explanations: A Perspective from Polytope Theory Pasan Dissanayake, Sanghamitra Dutta

NeurIPSW 2024 Model Reconstruction Using Counterfactual Explanations: A Perspective from Polytope Theory Pasan Dissanayake, Sanghamitra Dutta

ICMLW 2023 Demystifying Local and Global Fairness Trade-Offs in Federated Learning Using Information Theory Faisal Hamman, Sanghamitra Dutta

TMLR 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

UAI 2023 In- or Out-of-Distribution Detection via Dual Divergence Estimation Sahil Garg, Sanghamitra Dutta, Mina Dalirrooyfard, Anderson Schneider, Yuriy Nevmyvaka

ICML 2023 Robust Counterfactual Explanations for Neural Networks with Probabilistic Guarantees Faisal Hamman, Erfaun Noorani, Saumitra Mishra, Daniele Magazzeni, Sanghamitra Dutta

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

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

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

ICML 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, Kush Varshney

AISTATS 2018 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

NeurIPS 2016 Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover