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07/21/2023
Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
In this paper, we present an information-theoretic perspective to group ...
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05/19/2023
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
There is an emerging interest in generating robust counterfactual explan...
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02/02/2023
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Fair machine learning methods seek to train models that balance model pe...
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11/03/2022
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity
Existing regulations prohibit model developers from accessing protected ...
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07/06/2022
Robust Counterfactual Explanations for Tree-Based Ensembles
Counterfactual explanations inform ways to achieve a desired outcome fro...
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06/29/2022
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
The success of DNNs is driven by the counter-intuitive ability of over-p...
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06/16/2022
Quantifying Feature Contributions to Overall Disparity Using Information Theory
When a machine-learning algorithm makes biased decisions, it can be help...
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11/09/2021
Can Information Flows Suggest Targets for Interventions in Neural Circuits?
Motivated by neuroscientific and clinical applications, we empirically e...
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10/30/2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
There exist several methods that aim to address the crucial task of unde...
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04/30/2021
GTN-ED: Event Detection Using Graph Transformer Networks
Recent works show that the graph structure of sentences, generated from ...
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06/14/2020
Fairness Under Feature Exemptions: Counterfactual and Observational Measures
With the growing use of AI in highly consequential domains, the quantifi...
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03/23/2020
Slow and Stale Gradients Can Win the Race
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous ...
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10/17/2019
An Information-Theoretic Perspective on the Relationship Between Fairness and Accuracy
Our goal is to understand the so-called trade-off between fairness and a...
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03/04/2019
CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors
This work proposes the first strategy to make distributed training of ne...
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02/06/2019
Information Flow in Computational Systems
We develop a theoretical framework for defining and identifying flows of...
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11/28/2018
An Application of Storage-Optimal MatDot Codes for Coded Matrix Multiplication: Fast k-Nearest Neighbors Estimation
We propose a novel application of coded computing to the problem of the ...
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11/27/2018
A Unified Coded Deep Neural Network Training Strategy Based on Generalized PolyDot Codes for Matrix Multiplication
This paper has two contributions. First, we propose a novel coded matrix...
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03/03/2018
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous ...
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01/31/2018