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Found 9 papers, 2 papers with code
Quantifying Feature Contributions to Overall Disparity Using Information Theory
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.
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Motivated by neuroscientific and clinical applications, we empirically examine whether observational measures of information flow can suggest interventions.
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
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.
CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors
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
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.
On the Optimal Recovery Threshold of Coded Matrix Multiplication
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
Coded Distributed Computing for Inverse Problems
Our experiments for personalized PageRank performed on real systems and real social networks show that this ratio can be as large as $10^4$.
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
The fusion node can exploit this redundancy by completing the computation using outputs from only a subset of the processors, ignoring the stragglers.
On the Total-Power Capacity of Regular-LDPC Codes with Iterative Message-Passing Decoders
We analyze scaling behavior under two VLSI complexity models of decoding.
Information Theory Information Theory
