James Foulds | DeepAI

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Featured Co-authors

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∙ 05/06/2022

Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction

When a human receives a prediction or recommended course of action from ...

0 George J. Cancro, et al. ∙

∙ 06/13/2021

User Acceptance of Gender Stereotypes in Automated Career Recommendations

Currently, there is a surge of interest in fair Artificial Intelligence ...

0 Clarice Wang, et al. ∙

∙ 04/18/2021

Fair Representation Learning for Heterogeneous Information Networks

Recently, much attention has been paid to the societal impact of AI, esp...

0 Ziqian Zeng, et al. ∙

∙ 10/09/2020

Causal Feature Selection with Dimension Reduction for Interpretable Text Classification

Text features that are correlated with class labels, but do not directly...

0 Guohou Shan, et al. ∙

∙ 09/02/2020

Neural Fair Collaborative Filtering

A growing proportion of human interactions are digitized on social media...

0 Rashidul Islam, et al. ∙

∙ 01/09/2019

Estimating Buildings' Parameters over Time Including Prior Knowledge

Modeling buildings' heat dynamics is a complex process which depends on ...

0 Nilavra Pathak, et al. ∙

∙ 11/18/2018

Bayesian Modeling of Intersectional Fairness: The Variance of Bias

Intersectionality is a framework that analyzes how interlocking systems ...

0 James Foulds, et al. ∙

∙ 07/22/2018

An Intersectional Definition of Fairness

We introduce a measure of fairness for algorithms and data with regard t...

0 James Foulds, et al. ∙

∙ 05/20/2017

Mixed Membership Word Embeddings for Computational Social Science

Word embeddings improve the performance of NLP systems by revealing the ...

0 James Foulds, et al. ∙

∙ 11/01/2016

Variational Bayes In Private Settings (VIPS)

We provide a general framework for privacy-preserving variational Bayes ...

0 Mijung Park, et al. ∙

∙ 09/14/2016

Private Topic Modeling

We develop a privatised stochastic variational inference method for Late...

0 Mijung Park, et al. ∙

∙ 03/23/2016

On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis

Bayesian inference has great promise for the privacy-preserving analysis...

0 James Foulds, et al. ∙

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