Sanghamitra Dutta

CoRR, 2024

A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2024

Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss.

[BibT_eX]

[DOI]

Akshaj Kumar Veldanda

Trans. Mach. Learn. Res., 2023

A Review of Partial Information Decomposition in Algorithmic Fairness and Explainability.

[BibT_eX]

[DOI]

Entropy, 2023

Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition.

[BibT_eX]

[DOI]

CoRR, 2023

In- or out-of-distribution detection via dual divergence estimation.

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity.

[BibT_eX]

[DOI]

Jiahao Chen

Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023

REFRESH: Responsible and Efficient Feature Reselection guided by SHAP values.

[BibT_eX]

[DOI]

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

Strategies for Fair, Explainable, and Reliable Machine Learning Using Information Theory.

[BibT_eX]

[DOI]

PhD thesis, 2022

Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity.

[BibT_eX]

[DOI]

Jiahao Chen

CoRR, 2022

Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale.

[BibT_eX]

[DOI]

Akshaj Kumar Veldanda

CoRR, 2022

Quantifying Feature Contributions to Overall Disparity Using Information Theory.

[BibT_eX]

[DOI]

CoRR, 2022

MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues.

[BibT_eX]

[DOI]

Puneet Mathur

Atula Tejaswi Neerkaje

Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Robust Counterfactual Explanations for Tree-Based Ensembles.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

2021

Fairness Under Feature Exemptions: Counterfactual and Observational Measures.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2021

Slow and Stale Gradients Can Win the Race.

[BibT_eX]

[DOI]

Jianyu Wang

Gauri Joshi

IEEE J. Sel. Areas Inf. Theory, 2021

A Survey on the Robustness of Feature Importance and Counterfactual Explanations.

[BibT_eX]

[DOI]

CoRR, 2021

GTN-ED: Event Detection Using Graph Transformer Networks.

[BibT_eX]

[DOI]

CoRR, 2021

Can Information Flows Suggest Targets for Interventions in Neural Circuits?

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Information-Theoretic Measure for Enabling Category Exemptions with an Application to Filter Bubbles.

[BibT_eX]

[DOI]

Proceedings of the Advances in Bias and Fairness in Information Retrieval, 2021

2020

Information Flow in Computational Systems.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2020

On the Optimal Recovery Threshold of Coded Matrix Multiplication.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2020

Addressing Unreliability in Emerging Devices and Non-von Neumann Architectures Using Coded Computing.

[BibT_eX]

[DOI]

Proc. IEEE, 2020

How else can we define Information Flow in Neural Circuits?

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2020

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

An Information-Theoretic Quantification of Discrimination with Exempt Features.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

"Short-Dot": Computing Large Linear Transforms Distributedly Using Coded Short Dot Products.

[BibT_eX]

[DOI]

Viveck R. Cadambe

IEEE Trans. Inf. Theory, 2019

An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy.

[BibT_eX]

[DOI]

CoRR, 2019

CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors.

[BibT_eX]

[DOI]

CoRR, 2019

How should we define Information Flow in Neural Circuits?

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2019

2018

A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication.

[BibT_eX]

[DOI]

CoRR, 2018

A Unified Coded Deep Neural Network Training Strategy based on Generalized PolyDot codes.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Coded convolution for parallel and distributed computing within a deadline.

[BibT_eX]

[DOI]

Viveck R. Cadambe

Proceedings of the 2017 IEEE International Symposium on Information Theory, 2017

2016

Adaptivity provably helps: information-theoretic limits on $l_0$ cost of non-adaptive sensing.

[BibT_eX]

[DOI]

CoRR, 2016

Adaptivity provably helps: Information-theoretic limits on l0 cost of non-adaptive sensing.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Symposium on Information Theory, 2016

2014

LAMP: A Locally Adapting Matching Pursuit Framework for Group Sparse Signatures in Ultra-Wide Band Radar Imaging.

[BibT_eX]

[DOI]