Search Results for author: Sanghamitra Dutta

Found 14 papers, 2 papers with code

Robust Counterfactual Explanations for Tree-Based Ensembles

no code implementations6 Jul 2022 Sanghamitra Dutta, Jason Long, Saumitra Mishra, Cecilia Tilli, Daniele Magazzeni

In this work, we propose a novel strategy -- that we call RobX -- to generate robust counterfactuals for tree-based ensembles, e. g., XGBoost.

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

no code implementations29 Jun 2022 Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen, Sanghamitra Dutta, Alan Mishler, Siddharth Garg

We further show that MinDiff optimization is very sensitive to choice of batch size in the under-parameterized regime.


Quantifying Feature Contributions to Overall Disparity Using Information Theory

no code implementations16 Jun 2022 Sanghamitra Dutta, Praveen Venkatesh, Pulkit Grover

If we have access to the decision-making model, one potential approach (inspired from intervention-based approaches in explainability literature) is to vary each individual feature (while keeping the others fixed) and use the resulting change in disparity to quantify its contribution.

Decision Making

Can Information Flows Suggest Targets for Interventions in Neural Circuits?

1 code implementation NeurIPS 2021 Praveen Venkatesh, Sanghamitra Dutta, Neil Mehta, Pulkit Grover

Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions.


A Survey on the Robustness of Feature Importance and Counterfactual Explanations

no code implementations30 Oct 2021 Saumitra Mishra, Sanghamitra Dutta, Jason Long, Daniele Magazzeni

There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models.

Feature Importance

GTN-ED: Event Detection Using Graph Transformer Networks

no code implementations NAACL (TextGraphs) 2021 Sanghamitra Dutta, Liang Ma, Tanay Kumar Saha, Di Lu, Joel Tetreault, Alejandro Jaimes

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.

Event Detection

Fairness Under Feature Exemptions: Counterfactual and Observational Measures

no code implementations14 Jun 2020 Sanghamitra Dutta, Praveen Venkatesh, Piotr Mardziel, Anupam Datta, Pulkit Grover

While quantifying disparity is essential, sometimes the needs of an occupation may require the use of certain features that are critical in a way that any disparity that can be explained by them might need to be exempted.


Slow and Stale Gradients Can Win the Race

no code implementations23 Mar 2020 Sanghamitra Dutta, Jianyu Wang, Gauri Joshi

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in runtime as it waits for the slowest workers (stragglers).

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

no code implementations ICML 2020 Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney

Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset.

Fairness Two-sample testing

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

no code implementations4 Mar 2019 Sanghamitra Dutta, Ziqian Bai, Tze Meng Low, Pulkit Grover

This work proposes the first strategy to make distributed training of neural networks resilient to computing errors, a problem that has remained unsolved despite being first posed in 1956 by von Neumann.

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

no code implementations27 Nov 2018 Sanghamitra Dutta, Ziqian Bai, Haewon Jeong, Tze Meng Low, Pulkit Grover

First, we propose a novel coded matrix multiplication technique called Generalized PolyDot codes that advances on existing methods for coded matrix multiplication under storage and communication constraints.

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

no code implementations3 Mar 2018 Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, Priya Nagpurkar

Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers).

On the Optimal Recovery Threshold of Coded Matrix Multiplication

3 code implementations31 Jan 2018 Sanghamitra Dutta, Mohammad Fahim, Farzin Haddadpour, Haewon Jeong, Viveck Cadambe, Pulkit Grover

We provide novel coded computation strategies for distributed matrix-matrix products that outperform the recent "Polynomial code" constructions in recovery threshold, i. e., the required number of successful workers.

Information Theory Distributed, Parallel, and Cluster Computing Information Theory

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

no code implementations NeurIPS 2016 Sanghamitra Dutta, Viveck Cadambe, Pulkit Grover

The fusion node can exploit this redundancy by completing the computation using outputs from only a subset of the processors, ignoring the stragglers.

Distributed Computing

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